Both Keras models/layers (with the PyTorch backend) and Lightning Modules are PyTorch Modules, so they should be able to interoperate with each other in a PyTorch workflow. We have not tried this with Lightning, but we've had a good experience with custom PyTorch Modules.
More broadly, it's feasible to use Keras components with any framework built on PyTorch or JAX in the sense that it's always possible to write "adapter layers" that wrap a Keras layer and make it usable by another framework, or the other way around. We have folks doing this to use Flax components (from JAX) as Keras layers, and inversely, to use Keras layers as Flax Modules.
More broadly, it's feasible to use Keras components with any framework built on PyTorch or JAX in the sense that it's always possible to write "adapter layers" that wrap a Keras layer and make it usable by another framework, or the other way around. We have folks doing this to use Flax components (from JAX) as Keras layers, and inversely, to use Keras layers as Flax Modules.