What is the proper way to use early stopping with cross-validation?
I suspect this is a "no free lunch" situation, and the best thing to do is experiment with (subsets) of your data (or ideally, similar data disjoint from your training data) to see how the final model's ideal number of estimators compares to those of the cv iterations.

For example, if your validation performance rises sharply with additional estimators, then levels out, and finally decreases very slowly, then going too far isn't such a problem but cutting off early is.  If instead your validation performance grows slowly to a peak but then plummets with overfitting, then you'll want to set a smaller number of estimators for the final model.  And then there are all the other considerations for your model aside from straight validation score; maybe you're particularly averse to overfitting and want to set a smaller number of estimators, say the minimum among the cv iterations.

Another wrench: with more data, your model may want more estimators than any of the cv-estimates.  If you have the resources to experiment, also look into this.

Finally, you may consider leaving an early-stopping validation set aside even for the final model.  That trades away some extra training data for the convenience of not needing to estimate the optimal number of estimators as above.