Make zero_division parameter consistent in the different metric

[glemaitre]

This is an issue to report the step to actually take over the work of @marctorsoc in #23183 and split the PR into smaller one to facilitate the review process.

The intend is to make the zero_division parameter consistent across different metrics in scikit-learn. In this regards, we have the following TODO list:

• Introduce the zero_division parameter to the accuracy_score function when y_true and y_pred are empty.
• Introduce the zero_division parameter to the class_likelihood_ratios and remove raise_warning.
• Introduce the zero_division parameter to the cohen_kappa_score function.
• Introduce the zero_division parameter to the matthew_corr_coeff function ENH add zero_division=nan for classification metrics #23183.
• Open a PR to make sure the empty input lead to np.nan in classification_report function.

All those items have been addressed in #23183 and can be extracted in individual PRs. The changelog presenting the changes should acknowledge @marctorsoc.

In addition, we should investigate #27047 and check if we should add the zero_division parameter to the precision_recall_curve and roc_curve as well. This might add two additional items to the list above.

[glemaitre]

@StefanieSenger you might be interested in looking at some of the item.

[erikhuck]

+1 for this!