⚡ Bolt: Optimize clustering exact distances to inline squared checks

[teerthsharma]

💡 What: Replaced exact Euclidean distance calculations (libm::sqrt) with inline squared distance threshold checks in hot spatial scanning loops (auto_k_selection and DBSCAN::region_query). Also safely handles NaN and negative epsilon values.
🎯 Why: Computing libm::sqrt repeatedly in inner O(N^2) spatial queries introduces unnecessary computational overhead when the algorithms only use the distances internally for boolean threshold checks (distance < epsilon), not for their exact scalar values.
📊 Impact: Prevents costly floating-point math function calls for thousands of point pairs per clustering operation, measurably reducing CPU cycles on the critical path.
🔬 Measurement: Verify via cargo bench over the clustering module or by noting reduced CPU times in clustering-heavy workloads (e.g. test_auto_k). Tests passing ensure functional equivalence.

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PR created automatically by Jules for task 2372327294918612745 started by @teerthsharma

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