**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
airflow/models/dag.py
self, dag: DAG, most_recent_dag_run: Optional[pendulum.DateTime], active_runs_of_dag: int
    ) -> None:
        """
        Calculate ``next_dagrun`` and `next_dagrun_create_after``
        :param dag: The DAG object
        :param most_recent_dag_run: DateTime of most recent run of this dag, or none if not yet scheduled.
        :param active_runs_of_dag: Number of currently active runs of this dag
        """
        self.next_dagrun, self.next_dagrun_create_after = dag.next_dagrun_info(most_recent_dag_run)
        if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
            
            log.debug(
                "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                dag.dag_id,
                active_runs_of_dag,
                dag.max_active_runs,
            )
            self.next_dagrun_create_after = None
        log.info("Setting next_dagrun for %s to %s", dag.dag_id, self.next_dagrun)
def dag(*dag_args, **dag_kwargs):
    """
    Python dag decorator. Wraps a function into an Airflow DAG.
    Accepts kwargs for operator kwarg. Can be used to parametrize DAGs.
    :param dag_args: Arguments for DAG object
    :type dag_args: list
    :param dag_kwargs: Kwargs for DAG object.
    :type dag_kwargs: dict
    """
    def wrapper(f: Callable):