## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of burritos.
- $x_2$ represents the number of sandwiches.

## Step 2: Translate the objective function into symbolic notation
The objective is to maximize profit. Each burrito is sold for a profit of $2.5, and each sandwich is sold for a profit of $7. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 2.5x_1 + 7x_2 \]

## 3: Translate the constraints into symbolic notation
1. The restaurant has 5000 grams of pork meat. Each burrito requires 25 grams, and each sandwich requires 15 grams.
\[ 25x_1 + 15x_2 \leq 5000 \]
2. At least four times the number of sandwiches are needed than burritos.
\[ x_2 \geq 4x_1 \]
3. There needs to be at least 6 burritos made.
\[ x_1 \geq 6 \]
4. Non-negativity constraints (number of items cannot be negative):
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

However, since $x_1 \geq 6$ is given, the $x_1 \geq 0$ constraint is implicitly satisfied.

## 4: Compile the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'burritos'), ('x2', 'sandwiches')],
'objective_function': '2.5x1 + 7x2',
'constraints': [
'25x1 + 15x2 <= 5000',
'x2 >= 4x1',
'x1 >= 6'
]
}
```

## 5: Write the 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(name="burritos", lb=6, vtype=gurobi.GRB.INTEGER)  # At least 6 burritos
    x2 = model.addVar(name="sandwiches", vtype=gurobi.GRB.INTEGER)  # No lower bound specified, but implicitly non-negative

    # Objective function: Maximize 2.5x1 + 7x2
    model.setObjective(2.5 * x1 + 7 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(25 * x1 + 15 * x2 <= 5000, name="meat_constraint")
    model.addConstr(x2 >= 4 * x1, name="sandwich_burrito_ratio")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal number of burritos: {x1.varValue}")
        print(f"Optimal number of sandwiches: {x2.varValue}")
        print(f"Maximal profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```