Multi-GPU strategy scope causes save_weights error
Hi **@moberweger** ,

I have replicated the reported behaviour with colab using TF v2.14, 2.15. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/24ca1370e913df5ba221d609d8b0385a/62483_gpu-2-14-2-15.ipynb) here for reference.

Thank you! Hi @moberweger ,

I have done the code changes to the attached code to make it compatible to save and load in `.keras` format. 

Since there are custom objects in the model we need to explicitly override `get_config()` and `from_config()` methods. Please find the gists that executes fine with [keras3 ](https://colab.research.google.com/gist/SuryanarayanaY/a21c8f3b4327f4a41abc396af303df5b/62483_gpu_r1-keras3.ipynb)and [kears-2.15](https://colab.research.google.com/gist/SuryanarayanaY/577552b767bcdd5ecaaec35bedcc89eb/62483_gpu_r1-keras-2-15v.ipynb) as well.

Thanks! Thanks @SuryanarayanaY for looking into this.
I checked and the specific code works on my multi-GPU setup, but I observed some inconsistencies: 
1. It seems that the extension `.hdf5` vs `.h5` has an impact, since it still fails for `.hdf5` file format but it works on `.h5`
2. The gist for `keras-2.15` outputs `0 conv2d/kernel:0` whereas `keras-3.0` outputs `0 kernel`
3.  It is interesting to see that it also works without the `.keras` format

Thanks!
### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

2.13.1

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.8

### GPU model and memory

2x A6000 48G

### Current behavior?

I have a custom `Layer` with a `Model` that is created and saved within a multi-GPU scope. Everything works well without the  `MirroredStrategy` scope, thus running a single GPU. But when running on multiple GPUs using the strategy scope, the names of the weights of the model get truncated and essentially only resemble `kernel` and `bias`. This leads to duplicate entries in the `save_weights` function causing an HDF5 error. Code to reproduce is below (you need to have >=2 GPUs), and log output is also attached.

### Standalone code to reproduce the issue

```shell
import os
import shutil

import tensorflow as tf
from contextlib import nullcontext
from tensorflow.keras.layers import Input, Conv2D, Layer, GroupNormalization
from tensorflow.keras.models import Model, load_model


class MyLayer(Layer):

    def __init__(self, channels, **kwargs):
        super().__init__(**kwargs)
        self.norm = GroupNormalization(epsilon=1e-5)
        self.proj1 = Conv2D(channels, 1)
        self.proj2 = Conv2D(channels, 1)

    def call(self, inputs):
        return self.proj2(self.proj1(self.norm(inputs)))


class ModelMock(Model):

    def __init__(self, img_height, img_width, name=None):
        x_input = Input((img_height, img_width, 3), name="x_input")
        x = Conv2D(320, kernel_size=3, padding="same")(x_input)
        x = MyLayer(320)(x)
        output = Conv2D(16, kernel_size=3, padding="same")(x)
        super().__init__([x_input], output, name=name)


if __name__ == '__main__':
    strategy = tf.distribute.MirroredStrategy()
    print("Number of devices: {}".format(strategy.num_replicas_in_sync))
    if os.path.exists("./test"):
        shutil.rmtree("./test/")
    os.mkdir("./test/")

    fail = True
    with strategy.scope() if fail else nullcontext():
        print("CREATING")
        model = ModelMock(256, 256)
        for i, w in enumerate(model.weights): print(i, w.name)
        model.save_weights("./test/model1.hdf5")
        model.save("./test/model1")
        model = load_model("./test/model1", compile=False)
        for i, w in enumerate(model.weights): print(i, w.name)
        model.save_weights("./test/model2.hdf5")
    print("DONE")
```


### Relevant log output

```shell
Number of devices: 2
CREATING
0 conv2d/kernel:0
1 conv2d/bias:0
2 my_layer/group_normalization/gamma:0
3 my_layer/group_normalization/beta:0
4 my_layer/conv2d_1/kernel:0
5 my_layer/conv2d_1/bias:0
6 my_layer/conv2d_2/kernel:0
7 my_layer/conv2d_2/bias:0
8 conv2d_3/kernel:0
9 conv2d_3/bias:0
0 kernel:0
1 bias:0
2 gamma:0
3 beta:0
4 kernel:0
5 bias:0
6 kernel:0
7 bias:0
8 kernel:0
9 bias:0
Traceback (most recent call last):
  File "/home/naked/dev/meta-cv/nak3d-fusion/python/apps/ml/check_weight_names.py", line 48, in <module>
    model.save_weights("./test/model2.hdf5")
  File "/usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py", line 183, in create_dataset
    dsid = dataset.make_new_dset(group, shape, dtype, data, name, **kwds)
  File "/usr/local/lib/python3.8/dist-packages/h5py/_hl/dataset.py", line 163, in make_new_dset
    dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl, dapl=dapl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py/h5d.pyx", line 138, in h5py.h5d.create
ValueError: Unable to create dataset (name already exists)
```
