ComponentGraph `describe()` does not differentiate between duplicate components by name

When there are duplicate components, it is difficult to understand which component is being referenced by `describe()`.

```
# Using a more involved component graph with more complex edges
component_dict = {
"Imputer": ["Imputer", "X", "y"],
"Target Imputer": ["Target Imputer", "X", "y"],
"OneHot_RandomForest": ["One Hot Encoder", "Imputer.x", "Target Imputer.y"],
"OneHot_ElasticNet": ["One Hot Encoder", "Imputer.x", "y"],
"Random Forest": ["Random Forest Classifier", "OneHot_RandomForest.x", "y"],
"Elastic Net": ["Elastic Net Classifier", "OneHot_ElasticNet.x", "Target Imputer.y"],
"Logistic Regression": [
"Logistic Regression Classifier",
"Random Forest.x",
"Elastic Net.x",
"y",
],
}
cg_with_estimators = ComponentGraph(component_dict)
cg_with_estimators.instantiate({})
cg_with_estimators.describe()
```

returns:
```
1. Imputer
* categorical_impute_strategy : most_frequent
* numeric_impute_strategy : mean
* categorical_fill_value : None
* numeric_fill_value : None
2. Target Imputer
* impute_strategy : most_frequent
* fill_value : None
3. One Hot Encoder
* top_n : 10
* features_to_encode : None
* categories : None
* drop : if_binary
* handle_unknown : ignore
* handle_missing : error
4. One Hot Encoder
* top_n : 10
* features_to_encode : None
* categories : None
* drop : if_binary
* handle_unknown : ignore
* handle_missing : error
5. Random Forest Classifier
* n_estimators : 100
* max_depth : 6
* n_jobs : -1
6. Elastic Net Classifier
* penalty : elasticnet
* C : 1.0
* l1_ratio : 0.15
* n_jobs : -1
* multi_class : auto
* solver : saga
7. Logistic Regression Classifier
* penalty : l2
* C : 1.0
* n_jobs : -1
* multi_class : auto
* solver : lbfgs
```

There are two OHE in the graph but because we print the official component name, we do not know which OHE is referring to what (3 + 4). We should consider using or appending the name of the component as referenced by the ComponentGraph.