Numpy and JAX behaviours differ for sums over scalars.
### Description

When passed a scalar, `jax.numpy` cannot sum over the first nor last axis.

```python
import jax.numpy as jnp
import numpy as np

arr = 0.  # An array that is scalar for some reason
np.sum(arr, 0)  # no pb
np.sum(arr, -1)  # no pb

jnp.sum(arr, 0)  # raises
jnp.sum(arr, -1)  # raises
```

This is also valid for the `jnp.prod` function.
I believe this should likely be a valid input to `jnp.sum` WDYT?

### What jax/jaxlib version are you using?

jax==0.4.3 jaxlib==0.4.3+cuda11.cudnn86

### Which accelerator(s) are you using?

CPU/GPU

### Additional system info

_No response_

### NVIDIA GPU info

_No response_
diff --git a/jax/_src/numpy/reductions.py b/jax/_src/numpy/reductions.py
index 712aa4360..37d6d3fcd 100644
--- a/jax/_src/numpy/reductions.py
+++ b/jax/_src/numpy/reductions.py
@@ -657,6 +657,7 @@ def _make_cumulative_reduction(np_reduction: Any, reduction: Callable[..., Array
 
     if axis is None or _isscalar(a):
       a = lax.reshape(a, (np.size(a),))
+    if axis is None:
       axis = 0
 
     a_shape = list(np.shape(a))
['jax/_src/numpy/reductions.py']