Should one-hot encoded categorical features needs to be scaled when used along with text feature while deriving semantic similarity?
No, do not scale the one hot encoded vector with min-max scaling. That will lose the meaning of the data. One hot encoding means a data point is completely on a dimension or not. There is no meaning for those data points that are only fractional part of a dimension.
A better option to derive textual similarity using multiple features is to embedded all features in the same embedding space (including categorical features). StarSpace is one such embedding method.