To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's define:
- $x_1$ as the number of units of beans.
- $x_2$ as the number of units of onions.

The objective is to minimize the total cost of the burrito, which can be represented by the objective function: 
\[ \text{Minimize} \quad 6x_1 + 8x_2 \]

Given that one unit of beans contains 10 units of spice and 3 units of flavor, and one unit of onions contains 2 units of spice and 6 units of flavor, we have the following constraints to ensure the burrito contains at least 110 units of spice and 80 units of flavor:
\[ 10x_1 + 2x_2 \geq 110 \] (Spice constraint)
\[ 3x_1 + 6x_2 \geq 80 \] (Flavor constraint)

Additionally, since we cannot have negative units of beans or onions, we have non-negativity constraints:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

Thus, the symbolic representation of our problem is:

```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']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Burrito_Optimization")

# Define the variables
x1 = m.addVar(name="beans", vtype=GRB.CONTINUOUS, lb=0)
x2 = m.addVar(name="onions", vtype=GRB.CONTINUOUS, lb=0)

# Set the objective function
m.setObjective(6*x1 + 8*x2, GRB.MINIMIZE)

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Beans: {x1.x}")
    print(f"Onions: {x2.x}")
    print(f"Total Cost: {m.objVal}")
else:
    print("No optimal solution found")
```