perf(quickadapter): optimize Optuna log scale for LightGBM and CatBoost hyperparameters

perf(quickadapter): optimize Optuna log scale for LightGBM and CatBoost hyperparameters

Apply logarithmic sampling scale to regularization and tree complexity parameters for improved hyperparameter search efficiency:

- LightGBM: Add num_leaves to log scale (exponential tree growth)
- CatBoost: Add l2_leaf_reg and random_strength to log scale (multiplicative effects)
- Reverted bagging_temperature to linear scale (0 has special meaning: disables Bayesian bootstrap)

Log scale provides better exploration in low-value regions where these parameters have the most impact, consistent with Optuna best practices and industry standards (FLAML, XGBoost patterns).

Bump version to 3.10.6