Numpy and tf experimental Numpy differ in vander matrix creation case for N=0
@Ishticode,
I was able to reproduce the issue on tensorflow v2.11, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/d8153b468f2dbc37a09b12626f25a0cf/untitled1159.ipynb).
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

No

### Source

source

### Tensorflow Version

2.11.0

### Custom Code

Yes

### OS Platform and Distribution

Ubuntu 22.04 jammy

### Mobile device

_No response_

### Python version

3.8.10

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

11.6

### GPU model and memory

_No response_

### Current Behaviour?

The behaviour of `tf.experimental.numpy.vander` is different than `np.vander` for `N=0` where both value and shape of the output differ.

### Standalone code to reproduce the issue

```shell
import numpy as np        # 1.23.5
import tensorflow as tf   # 2.11.0

xn = np.array([1], dtype=np.int32)
x = tf.constant([1], dtype=tf.int32)
print(np.vander(xn, 0))
print()
print(tf.experimental.numpy.vander(x, 0))
```


### Relevant log output

```shell
[]

tf.Tensor([[1]], shape=(1, 1), dtype=int32)
```
</details>