**Apache Airflow version**: 2.0.0

**Environment**:

- **Cloud provider or hardware configuration**: Docker container
- **OS** (e.g. from /etc/os-release): Debian Stretch

**What happened**:

When working on some custom DAG override logic, I noticed that invoking `DAG.partial_subset` does not properly update the corresponding `_task_group.used_group_ids` on the returned subset DAG, such that adding back a task which was excluded during the `partial_subset` operation fails.

**What you expected to happen**:

Tasks that had already been added to the original DAG can be added again to the subset DAG returned by `DAG.partial_subset`

**How to reproduce it**:
Create any DAG with a single task called, e.g. `my-task`, then invoke `dag.partial_subset(['not-my-task'], False, False)`

Note that the returned subset DAG's `_task_group.used_group_ids` still contains `my-task` even though it was not included in the subset DAG itself

**Anything else we need to know**:

I was able to work around this by adding logic to update the new partial subset DAG's `_task_group.used_group_ids` manually, but this should really be done as part of the `DAG.partial_subset` logic
tests/models/test_dag.py
width = 5
        depth = 5
        weight = 5
        pattern = re.compile('stage(\\d*).(\\d*)')
        
        
        
        with DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) as dag:
            pipeline = [
                [DummyOperator(task_id=f'stage{i}.{j}', priority_weight=weight) for j in range(0, width)]
                for i in range(0, depth)
            ]
            for i, stage in enumerate(pipeline):
                if i == 0:
                    continue
                for current_task in stage:
                    for prev_task in pipeline[i - 1]:
                        current_task.set_upstream(prev_task)
            for task in dag.task_dict.values():
                match = pattern.match(task.task_id)
                task_depth = int(match.group(1))
                
                correct_weight = ((depth - (task_depth + 1)) * width + 1) * weight
                calculated_weight = task.priority_weight_total
                assert calculated_weight == correct_weight
    def test_dag_task_priority_weight_total_using_upstream(self):