1. MLForecast treats it more like a time series - it does differencing and moving averages as levels to encode the general level of each time series along with the ar lags. Not entirely necessary as you can just scale with like a standard scaler or even box cox at the time series level and pass a time series 'id' as a categorical variable to lightgbm and outperform MLForecast although it is pretty snappy with how they have it written.
2. I honestly just wouldn't use Prophet in general...But if you have 50 regressors it (I believe) fits them with a normal prior which is equivalent to a ridge regression so it shrinks the coefficients but you are stuck with this 'average' effect.
3. ARIMAX absolutely still has a place but it really all comes down to your features. If you you have good quality predictive features then it is usually better to do ML and 'featurize' the time pieces. You lose out on the time component but gain a lot due to the features. There are other issues like now you have to potentially forecast for those features. The alternative is having bad features. If that is the case then usually you are stuck with just standard time series methods. So it really is 100% dependent on your data and if there is use in learning stuff across multiple time series or not.

An alternative view is hierarchical forecasting which sometimes works well to take advantage of higher level seasonalities and trends that may be harder to see at the lower level and outperforms ML a good chunk in my experience unless you have good regressors.

As many are saying - SOTA are boosted trees with time features. If the features are bad then it is TS stuff like arimax. The best way to find out is to test each.

Edit: In regards to M5 - there was a lot of 'trickery' done to maximize the cost function there so it might not be 100% super useful, at least in my experience.