**Apache Airflow version**:
2.0.0

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
1.15
**Environment**:

- **Cloud provider or hardware configuration**: 
GKE
- **OS** (e.g. from /etc/os-release):
Ubuntu 18.04

**What happened**:
I just migrated from 1.10.14 to 2.0.0. When I turn on some random dags, the scheduler crashes with the following error:

```python
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1275, in _execute
    self._run_scheduler_loop()
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1377, in _run_scheduler_loop
    num_queued_tis = self._do_scheduling(session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1533, in _do_scheduling
    num_queued_tis = self._critical_section_execute_task_instances(session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1132, in _critical_section_execute_task_instances
    queued_tis = self._executable_task_instances_to_queued(max_tis, session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/utils/session.py", line 62, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1034, in _executable_task_instances_to_queued
    if task_instance.pool_slots > open_slots:
TypeError: '>' not supported between instances of 'NoneType' and 'int'
```
**What you expected to happen**:

I expected those dags would have their tasks scheduled without problems.

**How to reproduce it**:

Can't reproduce it yet. Still trying to figure out if this happens only with specific dags or not.

**Anything else we need to know**:

I couldn't find in which context `task_instance.pool_slots` could be None

airflow/models/taskinstance.py
delay_backoff_in_seconds = min(modded_hash, timedelta.max.total_seconds() - 1)
            delay = timedelta(seconds=delay_backoff_in_seconds)
            if self.task.max_retry_delay:
                delay = min(self.task.max_retry_delay, delay)
        return self.end_date + delay
    def ready_for_retry(self):
        """
        Checks on whether the task instance is in the right state and timeframe
        to be retried.
        """
        return self.state == State.UP_FOR_RETRY and self.next_retry_datetime() < timezone.utcnow()
    @provide_session
    def get_dagrun(self, session: Session = None):
        """
        Returns the DagRun for this TaskInstance
        :param session: SQLAlchemy ORM Session
        :return: DagRun
        """
        from airflow.models.dagrun import DagRun  
        dr = (
            session.query(DagRun)
            .filter(DagRun.dag_id == self.dag_id, DagRun.execution_date == self.execution_date)
            .first()
        )
        return dr
    @provide_session
    def check_and_change_state_before_execution(  
        self,
        verbose: bool = True,