[ENH] ForecastingOptunaSearchCV for hyper-parameter tuning for forecasting

[mk406]

Reference Issues/PRs

Closes #6618. See also #4188

What does this implement/fix? Explain your changes.

While sktime provides a ForecastingGridSearchCV, ForecastingRandomizedSearchCV and ForecastingSkoptSearchCV for grid search, there's a need for more sophisticated and efficient hyper-parameter optimization methods. The lack of integration with advanced optimization libraries like Optuna limits the ability to perform more complex and efficient hyper-parameter searches.
We propose the addition of a ForecastingOptunaSearchCV class to sktime's forecasting module. This class would leverage Optuna for hyper-parameter optimization, providing a more efficient and flexible way to find the best parameters for forecasting models. The interface is similar to the existing ForecastingGridSearchCV, allowing users to easily switch between grid search and Optuna-based optimization without significant changes to their code.

Does your contribution introduce a new dependency? If yes, which one?

Adds a dependency on Optuna.

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

We added test_optuna in /sktime/sktime/forecasting/model_selection/tests/test_tune.py

Any other comments?

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[fkiraly]

Code formatting tests are failing, see here how to automate code formatting for sktime code: https://www.sktime.net/en/stable/developer_guide/coding_standards.html