Whether this makes a difference, and how, will depend on the classifier you are using. For example, using a decision tree based model, or a simple linear model, scaling doesn't make any difference. If you are using some kind of regularized model, such as Ridge, some form of a bayesian model or a neural network, then scaling may affect your performance.
Now in general, your model predictions are never "correct". They may be reasonable, based on your model and the assumption that model makes, but also based on the expectation that your training data will be representative of the data you will encounter later.
Those are general remarkts, but I think they are important to keep in mind here. Now, more specifically, it is a bit unclear what you mean when you say
"because I know that for future trainings of the model, these values might appear"
If you are retraining the model, you should also refit the MinMaxScaler and it will adapt to new values. Then the above doesn't really apply and the best practice is to refit the feature processing with the new data.
scikit-learn has a great system for this called the pipeline, and the documentation may be helpful in any case, see https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html.
If you are not retraining the model, then you are saying that new data may not look like your testing data at all, and how bad that's going to affect your performance, as said before, will depend on a lot of things and may be hard to assess anyway. Maybe the feature that you are scaling is not important, and then it makes almost no difference. Maybe it's the only really important feature and you are using some nonlinear transformation of it (a squared feature that was in the range 0-1 now has a value 1000) and it will completely 0mess up the results of your classifier.
I think what you should do, in the face of changing data, is retrain the model on a regular basis, including the scaling part, and monitor it's performance.