It makes sense. There are situations where having multiple test datasets, each with a different distribution is useful :).
In your current formulation, two independent models are trained (model_1 and model_2), but none of them are evaluated on a test dataset. Here is something closer to what you describe:

# We assume the setup from the beginner colab (https://www.tensorflow.org/decision_forests/tutorials/beginner_colab)
train_ds_pd, test_ds_pd = split_dataset(...)

# In addition, here is the second dataset you mentioned: The "purely testing" set.
# Note: "test_ds_pd" is also a pure testing set.
pure_test_ds_pd = ...

# Train the model on the train split.
model = tfdf.keras.RandomForestModel()
model.fit(tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label))

# Add some metrics for the model evaluation.
model.compile(metrics=["accuracy", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])

# Evaluate the model on the test split of the first dataset.
evaluation_on_test = model.evaluate(tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label))

# Evaluate the model on the second dataset i.e. "the pure test" one.
evaluation_on_pure_test = model.evaluate(tfdf.keras.pd_dataframe_to_tf_dataset(pure_test_ds_pd, label=label))