Why Decision Tree boundary forms a square shape and SVM a circular/oval one?
Shape of the SVM decision boundary depends on the kernel (similarity function) used. The "standard" version of SVM has linear decision boundary. The one displayed could be using Gaussian kernel.

Decision boundary of a decision tree is determined by overlapping orthogonal half-planes (representing the result of each subsequent decision) and can end up as displayed on the pictures.

See more here:

https://shapeofdata.wordpress.com/2013/07/02/decision-trees/

https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM