Why CNN doesn't give higher accuracy over simple MLP network? [From Keras examples]
CNN (and RNN) models are not general improvements to the MLP design. They are specific choices that match certain types of problem. The CNN design works best when there is some local pattern in the data (which may repeat in other locations), and this is often the case when the inputs are images, audio or other similar signals.

The reuters example looks like a "bag of words" input. There is no local pattern or repeating relationships in that data that a CNN can take advantage of.

Your results with a CNN on this data set look reasonable to me. You have not made a mistake, but learned how a CNN really works on this data.