## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'green beans' and 'bowls of cereal', which we can denote as $x_1$ and $x_2$ respectively. The objective function to maximize is $6x_1 + 6x_2$. The constraints are:
- $12x_1 + 3x_2 \geq 31$ (total combined healthiness rating minimum)
- $14x_1 + 10x_2 \geq 16$ (total combined milligrams of calcium minimum)
- $10x_1 + 6x_2 \geq 13$ (total combined grams of carbohydrates minimum)
- $-2x_1 + 9x_2 \geq 0$ (specific linear constraint)
- $12x_1 + 3x_2 \leq 65$ (total combined healthiness rating maximum)
- $14x_1 + 10x_2 \leq 35$ (total combined milligrams of calcium maximum)
- $10x_1 + 6x_2 \leq 36$ (total combined grams of carbohydrates maximum)
- $x_1$ is an integer (green beans)
- $x_2$ can be fractional (bowls of cereal)

## Step 2: Convert the problem into a Gurobi-compatible format
We need to maximize $6x_1 + 6x_2$ subject to the given constraints.

## 3: Write down the symbolic representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'green beans'), ('x2', 'bowls of cereal')],
'objective_function': '6*x1 + 6*x2',
'constraints': [
    '12*x1 + 3*x2 >= 31',
    '14*x1 + 10*x2 >= 16',
    '10*x1 + 6*x2 >= 13',
    '-2*x1 + 9*x2 >= 0',
    '12*x1 + 3*x2 <= 65',
    '14*x1 + 10*x2 <= 35',
    '10*x1 + 6*x2 <= 36'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this in Gurobi using Python.

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="green_beans", vtype=gurobi.GRB.INTEGER)  # Green beans
    x2 = model.addVar(name="bowls_of_cereal")  # Bowls of cereal

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

    # Constraints
    model.addConstr(12*x1 + 3*x2 >= 31)  # Healthiness rating min
    model.addConstr(14*x1 + 10*x2 >= 16)  # Milligrams of calcium min
    model.addConstr(10*x1 + 6*x2 >= 13)  # Grams of carbohydrates min
    model.addConstr(-2*x1 + 9*x2 >= 0)  # Specific linear constraint
    model.addConstr(12*x1 + 3*x2 <= 65)  # Healthiness rating max
    model.addConstr(14*x1 + 10*x2 <= 35)  # Milligrams of calcium max
    model.addConstr(10*x1 + 6*x2 <= 36)  # Grams of carbohydrates max

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Green beans: {x1.varValue}")
        print(f"Bowls of cereal: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```