### Apache Airflow Provider(s)

microsoft-azure

### Versions of Apache Airflow Providers

```
apache-airflow-providers-amazon==7.4.0
apache-airflow-providers-common-sql==1.4.0
apache-airflow-providers-elasticsearch==4.4.0
apache-airflow-providers-ftp==3.3.1
apache-airflow-providers-http==4.3.0
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-microsoft-azure==5.3.0
apache-airflow-providers-microsoft-mssql==3.3.2
apache-airflow-providers-microsoft-winrm==3.1.1
apache-airflow-providers-odbc==3.2.1
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-salesforce==5.3.0
apache-airflow-providers-sftp==4.2.4
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.6.0
```

### Apache Airflow version

2.5.3

### Operating System

Debian GNU/Linux 11 (bullseye)

### Deployment

Virtualenv installation

### Deployment details

- Using PostgreSQL 14.7

### What happened

Starting `apache-airflow-providers-microsoft-azure==5.0.0`, the `get_link()` function doesn't return a URL value for the `AzureDataFactoryRunPipelineOperator` operator due to a class instance check in the commit [78b8ea2f22](https://github.com/apache/airflow/commit/78b8ea2f22239db3ef9976301234a66e50b47a94)

**Web server log :**
```
 {{data_factory.py:52}} INFO - The <class 'airflow.serialization.serialized_objects.SerializedBaseOperator'> is not <class 'airflow.providers.microsoft.azure.operators.data_factory.AzureDataFactoryRunPipelineOperator'> class.
```

### What you think should happen instead

An URL link should be generated during the run of the `AzureDataFactoryRunPipelineOperator`.

### How to reproduce

**DAG used to reproduce the problem :**

```
from airflow import DAG
from datetime import datetime, timedelta
from airflow.providers.microsoft.azure.operators.data_factory import AzureDataFactoryRunPipelineOperator

from airflow.models import Variable

import os

os.environ["HTTP_PROXY"] = "xxxx"
os.environ["HTTPS_PROXY"] = "xxxx"

with DAG(
    dag_id='azure_data_factory',
    default_args={
        'owner': 'airflow',
        'depends_on_past': False,
        'email_on_failure': False,
        'email_on_retry': False,
        'retries': 0,
        'retry_delay': timedelta(minutes=5),
    },
    start_date=datetime(2023, 1, 1),
    schedule=None,
    max_active_runs=1,
    catchup=False,
) as dag:
    run_test_pipeline = AzureDataFactoryRunPipelineOperator(
        task_id="run_test_pipeline",
        azure_data_factory_conn_id=Variable.get("ADF_CONNECTION_NAME"),
        pipeline_name=Variable.get("ADF_PIPELINE_NAME_DEMO"),
        wait_for_termination=True,
        check_interval=30
    )

    run_test_pipeline
```

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self.parameters = parameters
        self.timeout = timeout
        self.check_interval = check_interval
        self.deferrable = deferrable
    def execute(self, context: Context) -> None:
        self.hook = AzureDataFactoryHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
        self.log.info("Executing the %s pipeline.", self.pipeline_name)
        response = self.hook.run_pipeline(
            pipeline_name=self.pipeline_name,
            resource_group_name=self.resource_group_name,
            factory_name=self.factory_name,
            reference_pipeline_run_id=self.reference_pipeline_run_id,
            is_recovery=self.is_recovery,
            start_activity_name=self.start_activity_name,
            start_from_failure=self.start_from_failure,
            parameters=self.parameters,
        )
        self.run_id = vars(response)["run_id"]
        
        
        
        context["ti"].xcom_push(key="run_id", value=self.run_id)
        if self.wait_for_termination:
            if self.deferrable is False:
                self.log.info("Waiting for pipeline run %s to terminate.", self.run_id)
                if self.hook.wait_for_pipeline_run_status(
                    run_id=self.run_id,
                    expected_statuses=AzureDataFactoryPipelineRunStatus.SUCCEEDED,
                    check_interval=self.check_interval,
                    timeout=self.timeout,