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MinMaxScaler in scikit_learn is used for data normalization (a.k.a feature scaling). Data normalisation is not necessary for decision trees. Since XGBoost is based on decision trees, is it necessary to do data normalisation using MinMaxScaler for data to be fed to XGBoost machine learning models?

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Your rationale is indeed correct: decision trees do not require normalization of their inputs; and since XGBoost is essentially an ensemble algorithm comprised of decision trees, it does not require normalization for the inputs either.

For corroboration, see also the thread Is Normalization necessary? at the XGBoost Github repo, where the answer by the lead XGBoost developer is a clear:

no you do not have to normalize the features

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