For what condition boosting work better than bagging in Ensemble Learner?
Bagging (and features sampling) aim to reduce variance by providing low-correlated trees. Estimators can then be aggregated together to reduce variance. Reason is simple decision trees tend to quickly overfit in the bottom nodes. Bagging and features sampling only adress high-variance (overfitting) problems.

On the other hand, boosting is a meta-algorithm that aims to reduce both bias and variance by training estimators in a sequential way. Each predictor tries to reduce residuals from previous estimators.

In my opinion, the two meta-algorithms can't be easily compared that way. They will give you different results depending of data distributions, potential outliers, usecase... Potentially, boosting is more sensitive to outliers than bagging for example. I think trying the 2 methods is your best chance and you don't have to restrict to only one model.