⚡ Bolt: Optimize distance calculations in topological clustering

[teerthsharma]

💡 What: Replaced exact Euclidean distance calls in auto_k_selection and DBSCAN::region_query with inline squared distance calculation loops containing early exits, and fixed an epsilon control law bug in GeometricGovernor.
🎯 Why: In topological clustering, only neighborhood membership (d < epsilon) is required, not exact distance values. Computing libm::sqrt and iterating over all dimensions for distant points introduces unnecessary overhead on critical hot paths. The governor bug fix ensures the adaptive threshold controller properly handles high load.
📊 Impact: Significant reduction in computational overhead for high-dimensional clustering by bypassing expensive sqrt calls and redundant dimension summations for non-neighbors.
🔬 Measurement: Verify using cargo test -p aether-core and running profiling traces.

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PR created automatically by Jules for task 3324966427508775332 started by @teerthsharma

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