You have derivatives for a 16x16 image including derivatives for padding - but these are worthless. Padding is not a trainable parameter. It is also not a function of previous layers which means computing padding's derivatives is not necessary to do backpropagation for all layers. For these reasons you can just cut out the middle 14x14 matrix and pass it to the max pool layer. You don't have to calculate padding derivatives at all.

You have derivatives for a 16x16 image including derivatives for padding - but these are worthless. Padding is not a trainable parameter. It is also not a function of previous layers which means computing padding's derivatives is not necessary to do backpropagation for all layers. For these reasons you can just cut out the middle 14x14 matrix and pass it to the max pool layer.

To visualise it even better imagine that you have 1D convolution. Your maxpool outputs 14 values and you pad it to the left and to the right with a 0. It should look like this.

Now notice that during backprop when you have calculated your 16 derivatives to reverse concatenation you simply split your matrix to 1-14-1 and pass 14 variables to maxpool. You can pass the leftover derivatives to padding if for example they're trainable parameters but it usually isn't the case. That's why you don't have to calculate padding derivatives at all.