Yes both conclusions are correct, although the Random Forest implementation in scikit-learn makes it possible to enable or disable the bootstrap resampling.
In practice, RFs are often more compact than ETs. ETs are generally cheaper to train from a computational point of view but can grow much bigger. ETs can sometime generalize better than RFs but it’s hard to guess when it’s the case without trying both first (and tuning n_estimators, max_features and min_samples_split by cross-validated grid search).