Wrong gradient calculated for API tf.keras.layers.Add
Hi **@cheyennee** ,

I was able to reproduce the issue on colab using TF v2.14, 2.15, and TF-nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/9def4c1e50ace380e2e05d32980b89ab/62420_2-14_2-15-nightly_.ipynb) here for reference.

Thank you!
### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.14.0

### Custom code

Yes

### OS platform and distribution

windows colab

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Forward mode differentiation for the case is inconsistent with the gradient calculated in reverse mode. They shoule be the same.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
inputs_0_tensor = tf.random.uniform([], minval=0.3, maxval=1, dtype=tf.float32)
inputs_0 = tf.identity(inputs_0_tensor)
inputs_1_tensor = tf.random.uniform([], minval=1.1, maxval=10,dtype=tf.float32)
inputs_1 = tf.identity(inputs_1_tensor)
inputs = [inputs_0, inputs_1]
layer = tf.keras.layers.Add()

with tf.GradientTape(persistent=True, ) as g:
    g.watch(inputs)
    res_backward = layer(inputs)
grad_backward = g.jacobian(res_backward, res_backward)
print("res_backward:", res_backward)
print("grad_backward:", grad_backward)
tangent_1 = tf.constant(1,dtype=tf.float32)
tangent_2 = tf.constant(1,dtype=tf.float32)
tangents = [tangent_1, tangent_2]

with tf.autodiff.ForwardAccumulator(inputs, tangents) as acc:
    res_forward = layer(inputs)
    grad_jvp = acc.jvp(res_forward)
    print("res_forward:", res_forward)
    print("grad_forward", grad_jvp)
```


### Relevant log output

```shell
res_backward: tf.Tensor(9.794308, shape=(), dtype=float32)
grad_backward: tf.Tensor(1.0, shape=(), dtype=float32)
res_forward: tf.Tensor(9.794308, shape=(), dtype=float32)
grad_forward tf.Tensor(2.0, shape=(), dtype=float32)
```
