⚡ Bolt: [performance improvement] Vectorize scalar reductions in linalg

[teerthsharma]

💡 What: Replaced manual index-based loops (for i in 0..n) with functional iterator chains (.iter().zip().map().sum()) for scalar reductions in aether-core::ml::linalg (e.g., mse, mae, distances).
🎯 Why: Manual indexing into slices requires bounds checking on every iteration, which prevents LLVM from fully auto-vectorizing the reduction operations. Iterator chains elide these bounds checks.
📊 Impact: Improves execution speed of core mathematical reductions by enabling auto-vectorization, directly impacting the performance of ML algorithms and loss computations.
🔬 Measurement: Verified by running the core test suite (cargo test -p aether-core) to ensure no functional regressions occurred in the mathematical operations.

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PR created automatically by Jules for task 9919942073517875761 started by @teerthsharma

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