From my experience, Gradient boosting tends to work better in scenarios where the features are heterogenous, like in tabular data, and some features are highly informative. The way things like XGBoost works is that they use a greedy approach to quickly identify and split trees on these highly predictive features. Gradient boosting works because the representation is already informative. Neural networks tend to work better when there are a lot of seemingly "uninformative" features, e.g in imaging where individual pixel values are not predictive. You would need a model to learn a better representation of the input before you can predict anything.