## Problem Description and Formulation

The company wants to maximize the seating availability in the office by purchasing two types of desks: regular desks and standing desks, under certain constraints.

- Regular desks cost $150, take up 6 square feet of space, and seat 4 employees.
- Standing desks cost $200, take up 5 square feet of space, and seat 6 employees.
- The company can spend at most $2500.
- The office can have at most 250 square feet of desks.

Let's denote:
- \(R\) as the number of regular desks,
- \(S\) as the number of standing desks.

The objective is to maximize the total seating capacity, which is \(4R + 6S\).

The constraints are:
1. Budget constraint: \(150R + 200S \leq 2500\)
2. Space constraint: \(6R + 5S \leq 250\)
3. Non-negativity constraint: \(R \geq 0, S \geq 0\)
4. Integer constraint: \(R\) and \(S\) must be integers (since we can't buy fractions of a desk).

## Gurobi Code

To solve this linear programming problem (with integer constraints, making it a Mixed-Integer Linear Programming - MILP problem), we will use Gurobi's Python interface. Note that while the problem is formulated as an MILP due to the integer requirement for desk counts, Gurobi can handle this efficiently.

```python
import gurobi

def solve_desk_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    R = model.addVar(lb=0, vtype=gurobi.GRB.INTEGER, name="RegularDesks")
    S = model.addVar(lb=0, vtype=gurobi.GRB.INTEGER, name="StandingDesks")

    # Objective: Maximize seating capacity
    model.setObjective(4*R + 6*S, gurobi.GRB.MAXIMIZE)

    # Budget constraint
    model.addConstr(150*R + 200*S <= 2500, name="BudgetConstraint")

    # Space constraint
    model.addConstr(6*R + 5*S <= 250, name="SpaceConstraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {R.varName} = {R.x}, {S.varName} = {S.x}")
        print(f"Max seating capacity: {4*R.x + 6*S.x}")
    else:
        print("No optimal solution found")

solve_desk_problem()
```

This code defines the problem in Gurobi, solving for the optimal number of regular and standing desks that maximize seating capacity under the given constraints.