Consequence of Feature Scaling
This was meant as a comment but it is too long.

The fact that your test set has a different range might be a sign that the training set is not a good representation of the test set. However, if the difference is really small as in your example, it is likely that it won't affect your predictions. Unfortunately, I don't think I have a good reason to think it won't affect a SVM in any circumstance.

Notice that the rationale for using MinMaxScalar is (according to the documentation): 


  The motivation to use this scaling include robustness to very small
  standard deviations of features and preserving zero entries in sparse
  data.


Therefore, it is important for you to make sure that your data fits that case.

If you are really concerned about having a difference range, you should use a regular standardization (such as preprocessing.scale) instead.