For time series, is non stationary an issue for machine learning models like they are for traditional time series models like ARIMA? Sorry if the question seems basic. I understand that non stationary data is a big issue for traditional time series forecasting methods like ARIMA and VAR but is it the same for machine learning methods like XGBoost and neural networks? Meaning to say, do I have to do differencing on the y variable for machine learning methods like I do for ARIMA?

No, you don't have to do differencing when using XGBoost or neural networks. The idea with those data-driven models is that they have so many parameters that they can adapt to any property of the data. You don't have to presume any model, meaning you don't have to do the work to figure out an appropriate model. The disadvantage of data-driven models is that you learn much less about the "inner properties" of your data-generating system. But if you only care about prediction, that is totally fine.
