⚡ Bolt: Optimize spatial scans with inline squared distance comparisons

[teerthsharma]

💡 What: Replaced exact Euclidean distance calculations (libm::sqrt) in auto_k_selection and DBSCAN::region_query with inline squared distance comparisons. The optimization uses an inner loop early exit (!(sum < eps_sq)). Also fixed a misinitialized component counting variable in auto_k_selection.
🎯 Why: Exact distance calculations using libm::sqrt are a significant bottleneck in spatial scan algorithms (like DBSCAN) that compare distances against a constant threshold (epsilon). Calculating exact Euclidean distance does unnecessary work when the threshold constraint has already been violated by partial sums.
📊 Impact: Expected to significantly reduce CPU cycles in hot loops for threshold-based neighborhood queries by avoiding libm overhead and exiting early on large distance differentials.
🔬 Measurement: Verify using cargo test -p aether-core to ensure topological component counting and clustering still yield exactly identical labels and counts. Run performance benchmarks focusing on large K-selection grids.

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PR created automatically by Jules for task 5156532067478193291 started by @teerthsharma

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