[Perf #478] KMeans Digits fit: eliminate 5.9x training deficit while preserving clustering equivalence

Parent: #478

Canonical v2 Digits: Flow fit 351.94 ms vs sklearn 59.75 ms while Flow predict is faster (0.082 ms vs 0.132 ms). End-to-end is ~0.170x. Iris KMeans already wins ~6.7x, so the hole is scale-sensitive fit behavior.

Profile initialization, distance matrix/assignment, centroid accumulation, empty-cluster handling, convergence checks, restarts, allocation/copies and iteration count. Record distance evaluations and iterations so algorithmic work is separated from kernel throughput. Preserve the existing ARI/inertia semantic contract.

Acceptance: stage/iteration profile, operation counts, scaled samples/features/clusters/restarts, no regression in inertia/ARI parity, Digits fit >=0.95x first and >=1.05x target, current prediction win retained.

No file or test is named. Start by locating the KMeans fit implementation and the existing Digits and Iris performance checks, then profile the listed stages and record operation counts across the requested scales. Done means the profile and measurements are recorded, inertia and ARI parity remain intact, the stated fit targets are met, and the prediction advantage is preserved.