[Parity #478] Prove estimator hyperparameter, stopping-rule and work-budget equivalence in every performance row

Parent: #478.

A performance comparison is invalid if Flow and sklearn silently perform different configured work. Extend the parity contract to record and compare every performance-relevant setting: solver/algorithm, regularization, tolerance, max_iter, n_init, n_estimators, max_depth, feature subsampling, bootstrap, kernel/gamma, random seed, convergence/stopping rule and effective iteration/tree/support-vector counts.

Defaults must be materialized rather than assumed, because sklearn defaults can change by version.

Acceptance: each canonical/operation row contains resolved sklearn and Flow config plus effective-work diagnostics; mismatched work budgets are not performance-eligible; version changes that alter sklearn defaults fail/flag contract regeneration; benchmark docs expose deliberate semantic differences rather than hiding them.

Start with parent issue #478 and trace how canonical/operation performance rows are generated. Identify where sklearn and Flow configurations, defaults, and effective-work diagnostics are recorded, then inspect the benchmark documentation and contract-regeneration checks. Done means rows expose resolved settings and diagnostics, mismatched budgets are excluded, and default changes are flagged.