Is too much or very few training sample of a specific feature hamper the neural network model?
Theoretically speaking, there aren't any disadvantages to having too much or too few data. It will only reflect in the overall performance of your model. Based on the Sherlock paper, it seems that it's a choice they made for their preprocessing. This is their explanation:

Certain types occur more frequently in the VizNet corpus than
others. For example, description and city are more common
than collection and continent. To address this heterogeneity,
we limited the number of columns to at most 15K per class and
excluded the 10% types containing less than 1K columns

They did this to reduce the overall imbalance of their dataset.