found unexpected Nan result on API combination of tf.GradientTape() and tf.divide() both on GPU and CPU
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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf 2.13

### 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?

```python
import tensorflow as tf
import numpy as np

with tf.device("/cpu:0"):
  def test(x, y):
    with tf.GradientTape() as tape:
      tape.watch(x)
      tape.watch(y)
      z = tf.divide(x, y)
    jaco = tape.jacobian(z, x)
    return jaco
  x = tf.constant([1], dtype=tf.float32)
  y = tf.constant([0, 1, 2], dtype=tf.float32)
  cpu_output = test(x,y)

with tf.device("/gpu:0"):
  def test(x, y):
    with tf.GradientTape() as tape:
      tape.watch(x)
      tape.watch(y)
      z = tf.divide(x, y)
    jaco = tape.jacobian(z, x)
    return jaco

  x = tf.constant([1], dtype=tf.float32)
  y = tf.constant([0, 1, 2], dtype=tf.float32)
  gpu_output = test(x,y)

expected_output= tf.constant([[np.inf], [1 / 1], [1 / 2]])
print(cpu_output)
print(gpu_output)
print(expected_output)

### Standalone code to reproduce the issue

```shell
Unexpected NaN on the result both in GPU and CPU
```


### Relevant log output

```shell
tf.Tensor(
[[inf]
 [nan]
 [nan]], shape=(3, 1), dtype=float32)
tf.Tensor(
[[inf]
 [nan]
 [nan]], shape=(3, 1), dtype=float32)
tf.Tensor(
[[inf]
 [1. ]
 [0.5]], shape=(3, 1), dtype=float32)
```
