How to get non-normalized feature importances with random forest in scikit-learn
It seems you are normalizing the Gini dip for every tree and then summing those values.
 You should do the normalization at the end after the sum.
Result might vary depending upon the overall numbers in the two scenarios.
See this dummy example
import numpy as np
# Three Tree and 3 Features. F1 sum across Trees = 50, F2 = 48, F3 = 20
gini_dip = np.array([[20, 15, 5], [20, 15, 5], [10, 18, 10]]) 

# Normaliation before sum
gini_dip_n = np.array([((elem-min(elem))/(max(elem)-min(elem))) for elem in gini_dip])
fi_1 = gini_dip_n.sum(axis=0)

# Normaliation After sum
gini_dip_sum = gini_dip.sum(axis=0)
fi_2 = (gini_dip_sum - gini_dip_sum.min())/(gini_dip_sum.max() - gini_dip_sum.min())

fi_1, fi_2


(array([2.        , 2.33333333, 0.        ]),
 array([1.        , 0.93333333, 0.        ]))

F2 is best in scenario #1 and F1 is best in scenario #2