For xcoms pushed from different tasks, having the same execution_date and in the same Dag, the result of the xcom_pull with key set as None is not deterministic in its order

**Apache Airflow version**: 2.0


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

**Environment**:

- **Cloud provider or hardware configuration**:
- **OS** (e.g. from /etc/os-release):
- **Kernel** (e.g. `uname -a`):
- **Install tools**:
- **Others**:

**What happened**:
When tasks in the same dag push Xcoms, the result of the xcom_pull comes in any order. The order is not deterministic.

**What you expected to happen**:

I expected the order to be deterministic.

**How to reproduce it**:

Run the provided example_xcom dag in the repository multiple times


airflow/models/taskinstance.py
include_upstream=False,
                    include_direct_upstream=True,
                )
                dag_run.dag = partial_dag
                info = dag_run.task_instance_scheduling_decisions(session)
                skippable_task_ids = {
                    task_id
                    for task_id in partial_dag.task_ids
                    if task_id not in self.task.downstream_task_ids
                }
                schedulable_tis = [ti for ti in info.schedulable_tis if ti.task_id not in skippable_task_ids]
                for schedulable_ti in schedulable_tis:
                    if not hasattr(schedulable_ti, "task"):
                        schedulable_ti.task = self.task.dag.get_task(schedulable_ti.task_id)
                num = dag_run.schedule_tis(schedulable_tis)
                self.log.info("%d downstream tasks scheduled from follow-on schedule check", num)
                session.commit()
            except OperationalError as e:
                
                self.log.info(
                    f"Skipping mini scheduling run due to exception: {e.statement}",
                    exc_info=True,
                )
                session.rollback()
    def _prepare_and_execute_task_with_callbacks(self, context, task):
        """Prepare Task for Execution"""
        from airflow.models.renderedtifields import RenderedTaskInstanceFields
        task_copy = task.prepare_for_execution()
        self.task = task_copy
        def signal_handler(signum, frame):