[SPARK-50973][SQL][AVRO] Cleanup deprecated api usage related to avro.Schema#toString(boolean)

### What changes were proposed in this pull request?
This pr aims to use `SchemaFormatter.format(string, Schema)` instead of `avro.Schema#toString(boolean)`, the changes refer to

https://github.com/apache/avro/blob/8c27801dc8d42ccc00997f25c0b8f45f8d4a233e/lang/java/avro/src/main/java/org/apache/avro/Schema.java#L412-L422

![image](https://github.com/user-attachments/assets/ff4477af-dd49-40e6-a2ea-e6636e1e5ece)

### Why are the changes needed?
Cleanup deprecated api usage related to `avro.Schema#toString(boolean)`

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Pass GitHub Actions

### Was this patch authored or co-authored using generative AI tooling?
No

Closes apache#49640 from LuciferYang/avro-schema-toString.

Authored-by: yangjie01 <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

Author: LuciferYang

connector/avro/src/test/scala/org/apache/spark/sql/avro/AvroSuite.scala

@@ -25,7 +25,7 @@ import java.util.UUID
 
 import scala.jdk.CollectionConverters._
 
-import org.apache.avro.{AvroTypeException, Schema, SchemaBuilder}
+import org.apache.avro.{AvroTypeException, Schema, SchemaBuilder, SchemaFormatter}
 import org.apache.avro.Schema.{Field, Type}
 import org.apache.avro.Schema.Type._
 import org.apache.avro.file.{DataFileReader, DataFileWriter}
@@ -86,7 +86,7 @@ abstract class AvroSuite
   }
 
   def getAvroSchemaStringFromFiles(filePath: String): String = {
-    new DataFileReader({
+    val schema = new DataFileReader({
       val file = new File(filePath)
       if (file.isFile) {
         file
@@ -96,7 +96,8 @@ abstract class AvroSuite
           .filter(_.getName.endsWith("avro"))
           .head
       }
-    }, new GenericDatumReader[Any]()).getSchema.toString(false)
+    }, new GenericDatumReader[Any]()).getSchema
+    SchemaFormatter.format(AvroUtils.JSON_INLINE_FORMAT, schema)
   }
 
   // Check whether an Avro schema of union type is converted to SQL in an expected way, when the

sql/core/src/main/scala/org/apache/spark/sql/avro/AvroUtils.scala

@@ -21,7 +21,7 @@ import java.util.Locale
 
 import scala.jdk.CollectionConverters._
 
-import org.apache.avro.Schema
+import org.apache.avro.{Schema, SchemaFormatter}
 import org.apache.avro.file.{DataFileReader, FileReader}
 import org.apache.avro.generic.{GenericDatumReader, GenericRecord}
 import org.apache.avro.mapred.{AvroOutputFormat, FsInput}
@@ -44,6 +44,10 @@ import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
 private[sql] object AvroUtils extends Logging {
+
+  val JSON_INLINE_FORMAT: String = "json/inline"
+  val JSON_PRETTY_FORMAT: String = "json/pretty"
+
   def inferSchema(
       spark: SparkSession,
       options: Map[String, String],
@@ -71,7 +75,7 @@ private[sql] object AvroUtils extends Logging {
       case _ => throw new RuntimeException(
         s"""Avro schema cannot be converted to a Spark SQL StructType:
            |
-           |${avroSchema.toString(true)}
+           |${SchemaFormatter.format(JSON_PRETTY_FORMAT, avroSchema)}
            |""".stripMargin)
     }
   }

sql/core/src/main/scala/org/apache/spark/sql/avro/SchemaConverters.scala

@@ -22,7 +22,7 @@ import java.util.Locale
 import scala.collection.mutable
 import scala.jdk.CollectionConverters._
 
-import org.apache.avro.{LogicalTypes, Schema, SchemaBuilder}
+import org.apache.avro.{LogicalTypes, Schema, SchemaBuilder, SchemaFormatter}
 import org.apache.avro.LogicalTypes.{Decimal, _}
 import org.apache.avro.Schema.Type._
 import org.apache.avro.SchemaBuilder.FieldAssembler
@@ -148,9 +148,10 @@ object SchemaConverters extends Logging {
       case RECORD =>
         val recursiveDepth: Int = existingRecordNames.getOrElse(avroSchema.getFullName, 0)
         if (recursiveDepth > 0 && recursiveFieldMaxDepth <= 0) {
+          val formattedAvroSchema = SchemaFormatter.format(AvroUtils.JSON_PRETTY_FORMAT, avroSchema)
           throw new IncompatibleSchemaException(s"""
             |Found recursive reference in Avro schema, which can not be processed by Spark by
-            | default: ${avroSchema.toString(true)}. Try setting the option `recursiveFieldMaxDepth`
+            | default: $formattedAvroSchema. Try setting the option `recursiveFieldMaxDepth`
             | to 1 - $RECURSIVE_FIELD_MAX_DEPTH_LIMIT.
           """.stripMargin)
         } else if (recursiveDepth > 0 && recursiveDepth >= recursiveFieldMaxDepth) {

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala

@@ -23,13 +23,14 @@ import java.util.{List => JList, Map => JMap}
 
 import scala.jdk.CollectionConverters._
 
-import org.apache.avro.Schema
+import org.apache.avro.{Schema, SchemaFormatter}
 import org.apache.avro.generic.IndexedRecord
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 import org.apache.parquet.hadoop.ParquetWriter
 
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.avro.AvroUtils
 import org.apache.spark.sql.execution.datasources.parquet.test.avro._
 import org.apache.spark.sql.test.SharedSparkSession
 
@@ -40,7 +41,7 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with Shared
     logInfo(
       s"""Writing Avro records with the following Avro schema into Parquet file:
          |
-         |${schema.toString(true)}
+         |${SchemaFormatter.format(AvroUtils.JSON_PRETTY_FORMAT, schema)}
        """.stripMargin)
 
     val writer = AvroParquetWriter.builder[T](new Path(path)).withSchema(schema).build()