This same logic is partially applicable to gradient boosting regression trees (GBRTs).
While the basic notions presented are indeed pertinent for GBRTs as well, the basic reason why it isn't such a big problem is that if indeed a "very good split" is to be followed, we will find it with one of the subsequent learners. In contrast with random forests (RFs), where each base learner (tree) is oblivious to what other learners in the ensemble are doing, GBRT's base learners are aware of the residuals/gradients from the previous iterations/boosting rounds.
That said, tuning
min_gain_split touches upon a slightly different point: computation speed and tree-depth. On the one side,
num_leaves is a very "cheap" to compute and enforce metric; it will also indirectly and lightly constrain our overall tree depth (we can still have some loop-sided trees that get quite deep asymmetrically- I assume we are growing our trees leaf-wise instead of depth-wise) and it is very objective in the sense that it does not depend on the actual task, scale of data, etc. On the other side,
min_gain_split requires quite a bit of work to be computed (see the recent CV.SE thread: Gain vs Loss in terms of selecting best leaf split value for more details), it theoretically allows for "huge" base-learners (but that almost never happens in reality) and the actual value of it ($\gamma$ in XGBoost-speak) is task- & dataset- dependent so some subjectivity comes into play there.
I personally set
num_leaves at a high value (e.g.
max_depth to be proximal value to it (e.g.
10), and optimise against
min_data_in_leaf) as it guarantees shallower trees and combats over-fitting quite forcefully. It also gives me an idea of how much each learner can actually contribute. I have found though that if computational time is an issue
min_data_in_leaf again) allows for faster iterations and constraints the training time more aggressively. I don't think there is "one-size-fits-all" answer to this; as you correctly recognise both parameters regularise the individual base learner in a pruning-like manner. Maybe start with
num_leaves as it is harder to mess up a lot and move forward with
min_gain_split in a second round of modelling when some baselines are more clear.