**Apache Airflow version**: 2.0.0a1

**What happened**:

On my dev environment I have a lot of DAG runs with failed tasks, where the *downstream* task instances then have been set to `upstream_failed`. The scheduler is now refusing to schedule more instances for such DAGs.

The scheduler looks for such task instances that are not in the `State.finished` set:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/jobs/scheduler_job.py#L1482-L1488

However, `State.finished` is currently not a proper inverse of *running* states, it only includes success, failed, and skipped:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/utils/state.py#L110-L114

What is missing is the task instance `upstream_failed` state, so currently my log is being filled (very rapidly) with:

```
[2020-10-18 13:22:18,503] {{scheduler_job.py:1658}} INFO - DAG test_dag already has 26 active runs, not queuing any more tasks
```

Those 26 'active runs' are simply dag runs that failed and have 'upstream_failed' task instances.

Either `upstream_failed` should be added to `State.finished`, or the Scheduler task instance test should explicitly take that state into account.
airflow/models/dagrun.py
self,
        dag_id: Optional[str] = None,
        run_id: Optional[str] = None,
        execution_date: Optional[datetime] = None,
        start_date: Optional[datetime] = None,
        external_trigger: Optional[bool] = None,
        conf: Optional[Any] = None,
        state: Optional[str] = None,
        run_type: Optional[str] = None,
        dag_hash: Optional[str] = None,
        creating_job_id: Optional[int] = None,
    ):
        self.dag_id = dag_id
        self.run_id = run_id
        self.execution_date = execution_date
        self.start_date = start_date
        self.external_trigger = external_trigger
        self.conf = conf or {}
        self.state = state
        self.run_type = run_type
        self.dag_hash = dag_hash
        self.creating_job_id = creating_job_id
        super().__init__()
    def __repr__(self):