New parameters in final training
Unpopular opinion: Second quickest way to overfit (next to data-leakage) is hyper-parameter optimization.

Why? You are assuming you wont have covariate-shift, while in most of the cases you can bet on it. Hence optimising too much on train (available data) will be ruin.

The most reasonable assumption (that we have to make sure it stands) is that 60% of the data is representative of the 40% and also approximately of the future unseen data hence all of the 150 trees should catch all of the neccesarry information.