Is is possible to train on Hypergraphs in Keras?
As far as I'm aware, deep learning on hypergraphs is still a relatively new area, so I don't think there's any ready-made solution for hypergraphs. I did find this repo, which implements some models in keras to accompany a recent paper on hypergraph learning, but it is hardly a library.
You may also check out this paper, which cites a pair of techniques for converting a hypergraph to a graph:

Notably, previous methods [for hypergraph learning] typically decompose the hyperedge into pair-wise relationships where the decomposition methods can be divided into two categories: explicit and implicit. For instance, given a hyperedge (v_1,v_2,v_3), the explicit approach would decompose it directly into three edges, $(v_1,v_2), (v_2,v_3), (v_1,v_3)$, while the implicit approach would add a hidden node $e$ representing the hyperedge before decomposition, i.e., $(v_1,e),(v_2,e),(v_3,e)$.

If you're okay with doing this, then there are a few options besides Spektral:
Deep Graph Library provides a Tensorflow.keras compatible API, although their documentation seems to favor PyTorch.
There is also Graph Nets, which is built on top of Tensorflow.keras, although it's quite bare-bones at the moment.