StandardScaler and MinMaxScaler are more common when dealing with
continuous numerical data.
One possible preprocessing approach for OneHotEncoding scaling is "soft-binarizing" the dummy variables by converting
softb(0) = 0.1, softb(1) = 0.9. From my experience with feedforward Neural Networks this was found to be quite useful, so I expect it to be also benefitial for your MLPClassifier.
StandardScaler is useful for the features that follow a Normal distribution. This is clearly illustrated in the image below (source).
MinMaxScaler may be used when the upper and lower boundaries are well known from domain knowledge (e.g. pixel intensities that go from 0 to 255 in the RGB color range).