Scaling label encoded values for Linear Algorithms
Sounds like you should use a OneHotEncoder instead of a LabelEncoder since you are trying to encode non-ordinal data such as missing values. Also, one hot encoded values don't get affected by the MinMaxScaler, so that should be fine.

If MinMaxScaling makes sense is dependent on the categories. If your categories are ordinal, like [1,2,3] is [low, medium, high], then it still makes sense to use a LabelEncoder with a MinMaxScaler. 

But if you have non-ordinal categorical values, like [-1,0,1] for [MISSING, DOG, CAT], then it would be better to use a OneHotEncoder instead of forcing ordinality with a LabelEncoder. Otherwise the algorithms you will use will make the assumption that the distance MISSING-DOG is longer than MISSING-CAT, which makes no sense.