Scaling features separately?
Yes, these features should not be scaled together. Normally ALL features are scaled individually, so grouped scaling is the exception not the norm. From the Wikipedia on feature scaling:


  Since the range of values of raw data varies widely, in some machine
  learning algorithms, objective functions will not work properly
  without normalization. For example, the majority of classifiers
  calculate the distance between two points by the Euclidean distance.
  If one of the features has a broad range of values, the distance will
  be governed by this particular feature. Therefore, the range of all
  features should be normalized so that each feature contributes
  approximately proportionately to the final distance.


You can scale some features together if you know that they exist on the same scale, i.e. that "Sales January" is scaled together with "Sales February". But you should not scale things together that exist on different scales, i.e. "Sales January" and "Discount Ratio" should be scaled separately.