## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - $x_1$ = Number of beef burritos
  - $x_2$ = Number of pork burritos

- **Objective Function:**
  The objective is to maximize profit. Given that the profit per beef burrito is $3.5 and per pork burrito is $2.1, the objective function can be represented as:
  - Maximize $3.5x_1 + 2.1x_2$

- **Constraints:**
  - The food truck can make at most 100 burritos: $x_1 + x_2 \leq 100$
  - They must sell at least 20 beef burritos: $x_1 \geq 20$
  - They must sell at least 30 pork burritos: $x_2 \geq 30$
  - They can make at most 70 beef burritos: $x_1 \leq 70$
  - They can make at most 80 pork burritos: $x_2 \leq 80$

## Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'beef burritos'), ('x2', 'pork burritos')],
  'objective_function': '3.5*x1 + 2.1*x2',
  'constraints': [
    'x1 + x2 <= 100',
    'x1 >= 20',
    'x2 >= 30',
    'x1 <= 70',
    'x2 <= 80'
  ]
}
```

## 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="beef_burritos", lb=0, ub=100, vtype=gp.GRB.INTEGER)
    x2 = model.addVar(name="pork_burritos", lb=0, ub=100, vtype=gp.GRB.INTEGER)

    # Objective function: Maximize profit
    model.setObjective(3.5 * x1 + 2.1 * x2, gp.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 100, name="total_burritos")
    model.addConstr(x1 >= 20, name="min_beef_burritos")
    model.addConstr(x2 >= 30, name="min_pork_burritos")
    model.addConstr(x1 <= 70, name="max_beef_burritos")
    model.addConstr(x2 <= 80, name="max_pork_burritos")

    # Solve the model
    model.optimize()

    # Print solution
    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal Solution: Beef Burritos = {x1.varValue}, Pork Burritos = {x2.varValue}")
        print(f"Max Profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_burrito_problem()
```