What is a good approach for embedding both textual and spatial features for document classification?
When text is available as scanned image:

Divide your image into small grids.
Assign  each grid a row/column number like (i,j)
Now to your word vector append 2 more cells which are the row and column number of cell to which the word belongs.

When text is available as 
Have the document in the form of html. Then have the embeddings for entire html DOM tree of that document, which would include both  tags and the actual text. This way html tags would be giving the spatial/positional information.