To solve this problem, we need to maximize the profit from selling burritos and sandwiches while meeting the given constraints. Let's denote the number of burritos as $B$ and the number of sandwiches as $S$. 

The objective function to maximize is the total profit, which can be expressed as $2.5B + 7S$, since each burrito is sold for a profit of $2.5 and each sandwich for $7.

We have several constraints:
1. The total amount of pork meat used does not exceed 5000 grams. Since each burrito requires 25 grams and each sandwich requires 15 grams, this constraint can be written as $25B + 15S \leq 5000$.
2. At least four times the number of sandwiches are needed than burritos, which gives us $S \geq 4B$.
3. There needs to be at least 6 burritos made, so $B \geq 6$.

Now, let's translate these constraints and the objective function into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Pork_Meat_Optimization")

# Define variables
B = m.addVar(vtype=GRB.INTEGER, name="Burritos", lb=6)  # Number of burritos
S = m.addVar(vtype=GRB.INTEGER, name="Sandwiches")  # Number of sandwiches

# Objective function: Maximize profit
m.setObjective(2.5*B + 7*S, GRB.MAXIMIZE)

# Constraints
m.addConstr(25*B + 15*S <= 5000, "Pork_Meat_Limit")
m.addConstr(S >= 4*B, "Sandwich_to_Burrito_Ratio")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Burritos: {B.x}")
    print(f"Sandwiches: {S.x}")
    print(f"Maximum Profit: ${2.5*B.x + 7*S.x:.2f}")
else:
    print("No optimal solution found")
```