**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
"""
        Get a single DAG Run
        :param session: Sqlalchemy ORM Session
        :type session: Session
        :param dag_id: DAG ID
        :type dag_id: unicode
        :param execution_date: execution date
        :type execution_date: datetime
        :return: DagRun corresponding to the given dag_id and execution date
            if one exists. None otherwise.
        :rtype: airflow.models.DagRun
        """
        qry = session.query(DagRun).filter(
            DagRun.dag_id == dag_id,
            DagRun.external_trigger == False,  
            DagRun.execution_date == execution_date,
        )
        return qry.first()
    @property
    def is_backfill(self):