Add (built-in) multi-target support

Hi!

Thank you for providing this useful AutoML package.

In several of the main tree models (e.g. XGBoost 2.0, CatBoost) the regressors support fitting against multiple targets (see [here](https://catboost.ai/en/docs/concepts/loss-functions-multiregression) and [here](https://xgboost.readthedocs.io/en/stable/tutorials/multioutput.html)). With recent advances with `multi_output_tree`, these multi-output regression/classification models tend to be pretty useful.

Would it be possible to modify the AutoML class to support passing a 2-d "y" targets array-like input to `.fit()` for regression tasks ?

I think the implementation would require:
- modifying asserts / code checks to accept a 2-d multi-targets
- if multi-output input is detected: assert that the model list is supported (e.g. XGBoost & CatBoost work, whereas LightGBM does not yet have built-in support for it outside of wrapping with sklearn MultiOutput Regressor). Then modify default parameters to accomodate multi-target as needed, e.g. for CatBoost regression the default objective needs to be "MultiRMSE" instead of "RMSE". These are minor changes to default parametrization, if any.
- ensure splitters split y, if 2D, correctly and pass as-is to the underlying model(s)
- for cross-validate scores: define a default policy of e.g. averaging scores per target to be used as final tuning validation score

Doing this abstracts away implementation details of single vs multi-output for users and allows FLAML to "just work" for either

Thanks for considering,
Azmy