CategoryEncoding layer lacks consideration of 0-dimension inputs
Hi @AGFACBNNR ,

Thanks for reporting. I have checked and acknowledge the reported behaviour. The code works fine with Keras2 but has inconsistent behaviour with Keras3. Attached [keras2-gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/432b648914e4828b3b97fa586d60f288/64392_keras2.ipynb#scrollTo=m_Vhm9_MJoQm) and [keras3-gist ](https://colab.sandbox.google.com/gist/SuryanarayanaY/65ffaa8ac77949c929a20dc62d36824f/64392_keras3.ipynb#scrollTo=m_Vhm9_MJoQm)for reference.

As per [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding#:~:text=A%201D%20or%202D%20tensor%20of%20integer%20inputs.) the inputs should be:

> inputs:	A 1D or 2D tensor of integer inputs.

The behaviour as follows:
```
# rank-0 Tensor & output_mode='multi_hot'   --> Different results wrt Keras2 & 3
# rank-1 Tensor & output_mode='multi_hot'   --> Same results wrt Keras2 & 3
# rank-2 Tensor & output_mode='multi_hot'   --> Same results wrt Keras2 & 3

# rank-0 Tensor & output_mode='one_hot'     --> Different results wrt Keras2 & 3
# rank-1 Tensor & output_mode='one_hot'     --> Different results wrt Keras2 & 3
# rank-2 Tensor & output_mode='one_hot'     --> Same results wrt Keras2 & 3

```

IMO, This behaviour of Keras3 should be consistent with keras2. Looking for probable fix. 



 Update: Since Keras3 supports multi backend the Keras3(TF backend) behaviour is different wrt Keras2(TF backend) but inline with Torch and Jax.

But there is issue with `compute_output_shape` method with below cases which is not same as actual output shape:
```
# rank-0 Tensor input & output_mode='multi_hot'
# rank-0 Tensor input & output_mode='one_hot'
# rank-1 Tensor input & output_mode='one_hot'

```

I am going to propose a fix for same. Thanks! Shall be addressed in Keras PR-[19388](https://github.com/keras-team/keras/pull/19388) Hi @AGFACBNNR ,

The proposed PR got merged into master. Could you please test in Keras-nightly and let us know. Thanks!  I've tested it in Keras-nightly. Thanks a lot. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64392">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64392">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf-2.15, tf-2.16.1

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When provided a 0-dim tensor as an input, CategoryEncoding layer will generate an output whose shape is inconsistent with the result of `compute_output_shape()`. 
The document does not define the expected behavior of CategoryEncoding layer when the input is a 0-dim tensor. 
If it's not an acceptable input, maybe a Raise is needed, otherwise `compute_output_shape()` should have generated a shape matching the layer output.    

### Standalone code to reproduce the issue

```shell
You can find a simple example here:
https://colab.research.google.com/drive/1wYYiRlY_ehHz6WCAn0PMUXHycJ6Kfgk3?usp=sharing
```


### Relevant log output

_No response_