Support vector machine margin term, norm or norm squared?
It is indeed for computational tractability. You would not lose the all-important sparsity, since that is provided by the second term. The original formulation of SVM, which you can find on Wikipedia actually uses $\lVert w \rVert$:

Minimize $\|{\vec {w}}\|$ subject to ${\displaystyle y_{i}({\vec {w}}\cdot {\vec {x}}_{i}-b)\geq 1}-\xi_i$

Obviously, minimizing $\lVert w \rVert$ is the same as $\lVert w \rVert^2$. A quantative difference arises when introducing the constraint using the Lagrangian function, but qualitatively it is the same.