Bad dependency resolution in nested map

The simple program
```python
@dace.program
def bug(mat: dace.int32[N,N]):
buff = np.zeros_like(mat)
for i in dace.map[0:N]:
for j in dace.map[0:N]:
buff[i, j] = mat[i, j] * 2
for j in dace.map[0:N-1]:
mat[i, j] = buff[i, j]
```
will result in the following wrong simplified sdfg:
where the assign tasklet is a simple assignment operator, copying buff to mat

The issue does not happen if the two ranges of the internal maps (the ones with variable j) iterate over the same range. In the unsimplified nested sdfg representing the scope of the outer map (variable i) both buff and mat have two different names -> the state fusion fails to detect the data dependency, and ultimately one of the two maps is discarded.

Reproduce the issue with the Python program and inspect the unsimplified nested SDFG for the outer map. Trace state fusion and dependency handling for the two inner maps, then verify that simplification preserves the dependency between buff and mat and does not discard either map.

Written by the indexing model from the issue text.