## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- \(x_1\) as the units of beans
- \(x_2\) as the units of onions

The objective is to minimize the cost, where beans cost $6 per unit and onions cost $8 per unit. Therefore, the objective function can be represented as:

\[ \text{Minimize:} \quad 6x_1 + 8x_2 \]

The constraints are:

1. The burrito must contain a minimum of 110 units of spice. Given that one unit of beans contains 10 units of spice and one unit of onions contains 2 units of spice, we have:

\[ 10x_1 + 2x_2 \geq 110 \]

2. The burrito must contain a minimum of 80 units of flavor. Given that one unit of beans contains 3 units of flavor and one unit of onions contains 6 units of flavor, we have:

\[ 3x_1 + 6x_2 \geq 80 \]

3. Non-negativity constraints, as the units of beans and onions cannot be negative:

\[ x_1 \geq 0, \quad x_2 \geq 0 \]

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'units of beans'), ('x2', 'units of onions')],
    'objective_function': '6*x1 + 8*x2',
    'constraints': [
        '10*x1 + 2*x2 >= 110',
        '3*x1 + 6*x2 >= 80',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_burrito_problem():
    # Create a new model
    m = gurobi.Model()

    # Define variables
    x1 = m.addVar(name="beans", lb=0)  # Units of beans
    x2 = m.addVar(name="onions", lb=0)  # Units of onions

    # Objective function: Minimize cost
    m.setObjective(6 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    m.addConstr(10 * x1 + 2 * x2 >= 110, name="spice_constraint")
    m.addConstr(3 * x1 + 6 * x2 >= 80, name="flavor_constraint")

    # Optimize
    m.optimize()

    # Print solution
    if m.status == gurobi.GRB.OPTIMAL:
        print("Optimal cost: ", m.objVal)
        print("Units of beans: ", x1.varValue)
        print("Units of onions: ", x2.varValue)
    else:
        print("The problem is infeasible")

solve_burrito_problem()
```