Calling `to_block()` results in duplicate node types -- breaking several parts of heterograph

## 🐛 Bug

Calling `to_block()` results in duplicate node types, breaking things like edge functions, as assigning features via `ndata` only operates on the `srcnodes`, and not the `dstnodes`.

At least, the implementation of `DGLHeteroGraph.get_ntype_id()` is broken by this:
```
def get_ntype_id(self, ntype):
...
ntid = self._srctypes_invmap.get(ntype, self._dsttypes_invmap.get(ntype, None))
```
As it will only ever choose the source node type, and not the destination node type. There may be other such things that are broken as well.

## To Reproduce

```
import dgl
import torch as th
g = dgl.heterograph({('user','follows','user'): [(0,1), (1,0), (0,2)]})
print("graph ntypes = {}".format(g.ntypes))
b = dgl.to_block(g)
print("block ntypes = {}".format(b.ntypes))
```

Results in:
```
Using backend: pytorch
graph ntypes = ['user']
block ntypes = ['user', 'user']
```

## Expected behavior

My understanding of how heterograph is implemented, is for it to work properly, we would expect one edge type in the block:
```
Using backend: pytorch
graph ntypes = ['user']
block ntypes = ['user']
```

## Environment

- DGL Version (e.g., 1.0): master branch
- Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): pytorch 1.8
- OS (e.g., Linux): ubuntu 18.04
- How you installed DGL (`conda`, `pip`, source): source
- Build command you used (if compiling from source): cmake .. -DUSE_CUDA=ON -DCMAKE_BUILD_TYPE=Debug
- Python version: 3.6
- CUDA/cuDNN version (if applicable): 11.3
- GPU models and configuration (e.g. V100): TitanV
- Any other relevant information:

## Additional context

This is currently a blocker for edge property prediction on a homogeneous graph with sampling.