## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - $x_1$: tacos
  - $x_2$: burritos

- **Objective Function:** The profit per taco is $3, and the profit per burrito is $6. The goal is to maximize profit.
  - Maximize: $3x_1 + 6x_2$

- **Constraints:**
  - Sell at least 50 tacos: $x_1 \geq 50$
  - Sell at least 30 burritos: $x_2 \geq 30$
  - At most 80 tacos: $x_1 \leq 80$
  - At most 50 burritos: $x_2 \leq 50$
  - At most 100 items total: $x_1 + x_2 \leq 100$

## Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'tacos'), ('x2', 'burritos')],
  'objective_function': '3*x1 + 6*x2',
  'constraints': [
    'x1 >= 50',
    'x2 >= 30',
    'x1 <= 80',
    'x2 <= 50',
    'x1 + x2 <= 100'
  ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

def solve_food_truck_problem():
    # Create a new model
    model = gp.Model("food_truck")

    # Define variables
    x1 = model.addVar(name="tacos", lb=0, ub=100, vtype=gp.GRB.INTEGER)
    x2 = model.addVar(name="burritos", lb=0, ub=100, vtype=gp.GRB.INTEGER)

    # Objective function: Maximize profit
    model.setObjective(3*x1 + 6*x2, gp.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 >= 50, name="min_tacos")
    model.addConstr(x2 >= 30, name="min_burritos")
    model.addConstr(x1 <= 80, name="max_tacos")
    model.addConstr(x2 <= 50, name="max_burritos")
    model.addConstr(x1 + x2 <= 100, name="total_items")

    # Solve the model
    model.optimize()

    # Print solution
    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal solution: tacos = {x1.varValue}, burritos = {x2.varValue}")
        print(f"Max profit: ${3*x1.varValue + 6*x2.varValue}")
    else:
        print("No optimal solution found.")

solve_food_truck_problem()
```