## Problem Description and Symbolic Representation

The problem involves maximizing profit by determining the number of bean burritos and beef burritos to make, given certain constraints on ingredients and demand.

### Symbolic Variables:
- $x_1$ = Number of bean burritos
- $x_2$ = Number of beef burritos

### Objective Function:
Maximize profit $P = 6.5x_1 + 9x_2$

### Constraints:
1. Lettuce constraint: $25x_1 + 18x_2 \leq 5000$
2. Demand constraint for beef burritos vs bean burritos: $x_2 \geq 4x_1$
3. Minimum bean burritos: $x_1 \geq 5$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

However, since $x_1$ and $x_2$ represent quantities of burritos, the non-negativity constraints are implicitly satisfied by the minimum bean burritos constraint and the nature of $x_2 \geq 4x_1$.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'bean burritos'), ('x2', 'beef burritos')],
    'objective_function': '6.5*x1 + 9*x2',
    'constraints': [
        '25*x1 + 18*x2 <= 5000',
        'x2 >= 4*x1',
        'x1 >= 5'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

def solve_burrito_problem():
    # Create a new model
    model = gp.Model("Burrito_Problem")

    # Define variables
    x1 = model.addVar(name="bean_burritos", lb=5, vtype=gp.GRB.INTEGER)  # At least 5 bean burritos
    x2 = model.addVar(name="beef_burritos", vtype=gp.GRB.INTEGER)  # No lower bound, but constrained by others

    # Objective function: Maximize profit
    model.setObjective(6.5 * x1 + 9 * x2, gp.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(25 * x1 + 18 * x2 <= 5000, name="Lettuce_Constraint")
    model.addConstr(x2 >= 4 * x1, name="Demand_Constraint")

    # Solve the problem
    model.optimize()

    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal solution: Bean Burritos = {x1.varValue}, Beef Burritos = {x2.varValue}")
        print(f"Max Profit: ${model.objVal:.2f}")
    else:
        print("The problem is infeasible.")

solve_burrito_problem()
```