Why do the deconv outputs of layer >= 2 of Zeiler&Fergus look so unrealistic?
The reconstructed images from deconvnet represent the patterns in the original images which are very likely to strongly activate a particular featuremap at a particular convolution layer.

The gray portion in the reconstructed images shows that the values of those pixels in input images are not likely to strongly activate that featuremap.

There is no such gray portion in the first layer filters because first layer filter are representing the very simple patterns(e.g. edges) and those are very likely to present in input image at everywhere.