**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
return f"<TaskInstance: {self.dag_id}.{self.task_id} {self.execution_date} [{self.state}]>"
    def next_retry_datetime(self):
        """
        Get datetime of the next retry if the task instance fails. For exponential
        backoff, retry_delay is used as base and will be converted to seconds.
        """
        delay = self.task.retry_delay
        if self.task.retry_exponential_backoff:
            
            
            
            min_backoff = int(math.ceil(delay.total_seconds() * (2 ** (self.try_number - 2))))
            
            ti_hash = int(
                hashlib.sha1(
                    "{}#{}#{}#{}".format(
                        self.dag_id, self.task_id, self.execution_date, self.try_number  
                    ).encode('utf-8')
                ).hexdigest(),
                16,
            )
            
            modded_hash = min_backoff + ti_hash % min_backoff