## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of regular desks
- $x_2$ represents the number of standing desks

The objective is to maximize the seating availability. Regular desks seat 4 employees, and standing desks seat 6 employees. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 4x_1 + 6x_2 \]

## Step 2: Define the constraints

The company can spend at most $2500. Regular desks cost $150, and standing desks cost $200. This gives us the cost constraint:
\[ 150x_1 + 200x_2 \leq 2500 \]

The office can have at most 250 square feet of desks. Regular desks take up 6 square feet, and standing desks take up 5 square feet. This gives us the space constraint:
\[ 6x_1 + 5x_2 \leq 250 \]

Also, $x_1$ and $x_2$ must be non-negative integers since they represent the number of desks:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]
\[ x_1 \in \mathbb{Z}, \quad x_2 \in \mathbb{Z} \]

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'regular desks'), ('x2', 'standing desks')],
    'objective_function': '4*x1 + 6*x2',
    'constraints': [
        '150*x1 + 200*x2 <= 2500',
        '6*x1 + 5*x2 <= 250',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Gurobi code in Python

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="regular_desks", vtype=gurobi.GRB.INTEGER, lb=0)
    x2 = model.addVar(name="standing_desks", vtype=gurobi.GRB.INTEGER, lb=0)

    # Objective function: Maximize 4*x1 + 6*x2
    model.setObjective(4*x1 + 6*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(150*x1 + 200*x2 <= 2500, name="budget_constraint")
    model.addConstr(6*x1 + 5*x2 <= 250, name="space_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of regular desks: {x1.varValue}")
        print(f"Number of standing desks: {x2.varValue}")
        print(f"Maximum seating availability: {4*x1.varValue + 6*x2.varValue}")
    else:
        print("No optimal solution found.")

solve_office_space_design()
```