Normalization for a 2d input array
Dense layers inherently work with 1d-data and lose any positional &quot;importance&quot; of your input data (e.g. the location of a person in an image, how digits come together to form a number). Theoretically, you'll achieve the same training output if you decided to randomly shuffle the positions of your input features. If you want to stick with a Dense model as you've provided, I believe that normalizing on a 2d array would not benefit your training.
But it sounds like the position of each feature in the 6x25 matrix is important for your predictions. In that case, I would suggest switching from a Dense model to a convolutional model, which incorporates the position of each feature during training. Here's an example. In this case, here's a useful list of normalization techniques for 2D or 3D data inputs.
Please let me know if I can clarify anything. Good luck!