When should I use Gini Impurity as opposed to Information Gain (Entropy)?
Gini impurity and Information Gain Entropy are pretty much the same. And people do use the values interchangeably. Below are the formulae of both:


$\textit{Gini}: \mathit{Gini}(E) = 1 - \sum_{j=1}^{c}p_j^2$
$\textit{Entropy}: H(E) = -\sum_{j=1}^{c}p_j\log p_j$


Given a choice, I would use the Gini impurity, as it doesn't require me to compute logarithmic functions, which are computationally intensive. The closed form of it's solution can also be found.


  Which metric is better to use in different scenarios while using
  decision trees ?


The Gini impurity, for reasons stated above.

So, they are pretty much same when it comes to CART analytics.

Helpful reference for computational comparison of the two methods