You are given an integer side, representing the edge length of a square with corners at (0, 0), (0, side), (side, 0), and (side, side) on a Cartesian plane.

You are also given a positive integer k and a 2D integer array points, where points[i] = [x_i, y_i] represents the coordinate of a point lying on the boundary of the square.

You need to select k elements among points such that the minimum Manhattan distance between any two points is maximized.

Return the maximum possible minimum Manhattan distance between the selected k points.

The Manhattan Distance between two cells (x_i, y_i) and (x_j, y_j) is |x_i - x_j| + |y_i - y_j|.

Example 1:

Input: side = 2, points = [[0,2],[2,0],[2,2],[0,0]], k = 4

Output: 2

Explanation:

Select all four points.

Example 2:

Input: side = 2, points = [[0,0],[1,2],[2,0],[2,2],[2,1]], k = 4

Output: 1

Explanation:

Select the points (0, 0), (2, 0), (2, 2), and (2, 1).

Example 3:

Input: side = 2, points = [[0,0],[0,1],[0,2],[1,2],[2,0],[2,2],[2,1]], k = 5

Output: 1

Explanation:

Select the points (0, 0), (0, 1), (0, 2), (1, 2), and (2, 2).

Constraints:

1 <= side <= 10⁹
4 <= points.length <= min(4 * side, 15 * 10³)
points[i] == [xi, yi]
The input is generated such that:
- points[i] lies on the boundary of the square.
- All points[i] are unique.
4 <= k <= min(25, points.length)