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

Let's denote:
- $x_1$ as the number of subs
- $x_2$ as the number of flatbreads

The profit per sub is $3, and the profit per flatbread is $2.50. The objective is to maximize profit, so the objective function is $3x_1 + 2.5x_2$.

## Step 2: Define the constraints

Each sub takes 3 minutes of preparation and each flatbread takes 4 minutes of preparation, with 2000 minutes available for preparation. This gives us the constraint $3x_1 + 4x_2 \leq 2000$.

Each sub takes 2 minutes of toasting and each flatbread takes 1 minute of toasting, with 2200 minutes available for toasting. This gives us the constraint $2x_1 + x_2 \leq 2200$.

Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of subs and flatbreads cannot be negative.

## 3: Symbolic representation

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'subs'), ('x2', 'flatbreads')],
    'objective_function': '3*x1 + 2.5*x2',
    'constraints': [
        '3*x1 + 4*x2 <= 2000',
        '2*x1 + x2 <= 2200',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Gurobi code

Now, let's write the Gurobi code in Python to solve this problem:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="subs")
    x2 = model.addVar(lb=0, name="flatbreads")

    # Set the objective function
    model.setObjective(3*x1 + 2.5*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3*x1 + 4*x2 <= 2000, name="preparation_time")
    model.addConstr(2*x1 + x2 <= 2200, name="toasting_time")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: subs = {x1.varValue}, flatbreads = {x2.varValue}")
        print(f"Maximum profit: ${3*x1.varValue + 2.5*x2.varValue:.2f}")
    else:
        print("No optimal solution found")

solve_optimization_problem()
```