According to the documentation, padding to maximum length is not possible, when the TextVectorization layer output mode is ‘int’. So you have to use this layer separately from the main model and then apply tf.keras.preprocessing.sequence.pad_sequences to the input data.
Probably, you could create a custom padding layer by subclussing keras.layers.Layer and using sequence.pad_sequences in the call method. I did not try it, but if it works, all the layers could be saved as a single model in one file.