**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
start_date = Column(UtcDateTime, default=timezone.utcnow)
    end_date = Column(UtcDateTime)
    _state = Column('state', String(50), default=State.RUNNING)
    run_id = Column(String(ID_LEN))
    creating_job_id = Column(Integer)
    external_trigger = Column(Boolean, default=True)
    run_type = Column(String(50), nullable=False)
    conf = Column(PickleType)
    
    last_scheduling_decision = Column(UtcDateTime)
    dag_hash = Column(String(32))
    dag = None
    __table_args__ = (
        Index('dag_id_state', dag_id, _state),
        UniqueConstraint('dag_id', 'execution_date'),
        UniqueConstraint('dag_id', 'run_id'),
        Index('idx_last_scheduling_decision', last_scheduling_decision),
    )
    task_instances = relationship(
        TI,
        primaryjoin=and_(TI.dag_id == dag_id, TI.execution_date == execution_date),  
        foreign_keys=(dag_id, execution_date),
        backref=backref('dag_run', uselist=False),
    )
    DEFAULT_DAGRUNS_TO_EXAMINE = airflow_conf.getint(
        'scheduler',
        'max_dagruns_per_loop_to_schedule',
        fallback=20,
    )
    def __init__(