## Step 1: Define the symbolic representation of the variables
The variables are 'peanutbutter sandwiches', 'pickles', and 'green beans', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9x_0^2 + 9x_0x_1 + 5x_0x_2 + 5x_1^2 + 4x_1x_2 + x_0 + 7x_1 + 2x_2$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $12x_0 = 12$ (tastiness rating of peanutbutter sandwiches)
- $2x_0 = 2$ (grams of fat in peanutbutter sandwiches)
- $x_1 = 1$ (tastiness rating of pickles)
- $4x_1 = 4$ (grams of fat in pickles)
- $13x_2 = 13$ (tastiness rating of green beans)
- $12x_2 = 12$ (grams of fat in green beans)
- $12x_0 + 13x_2 \geq 15$ (total combined tastiness rating from peanutbutter sandwiches and green beans)
- $12x_0 + x_1 + 13x_2 \geq 15$ (total combined tastiness rating from peanutbutter sandwiches, pickles, and green beans)
- $4x_1 + 12x_2 \geq 19$ (total combined grams of fat from pickles and green beans)
- $2x_0 + 4x_1 \geq 15$ (total combined grams of fat from peanutbutter sandwiches and pickles)
- $2x_0 + 4x_1 + 12x_2 \geq 15$ (total combined grams of fat from peanutbutter sandwiches, pickles, and green beans)
- $10x_1 - 5x_2 \geq 0$ (relationship between pickles and green beans)
- $2x_0 + 4x_1 + 12x_2 \leq 138$ (total grams of fat)

## 4: Correct and simplify constraints based on given attributes
Given attributes:
- $r0$: tastiness rating, $x_0 = 12$, $x_1 = 1$, $x_2 = 13$
- $r1$: grams of fat, $x_0 = 2$, $x_1 = 4$, $x_2 = 12$

These imply:
- The tastiness rating and fat content are fixed per item, not per unit variable.

## 5: Formulate the problem in Gurobi
Given the complexity and specific requirements, let's directly formulate the problem.

## 6: Symbolic Representation
```json
{
    'sym_variables': [('x0', 'peanutbutter sandwiches'), ('x1', 'pickles'), ('x2', 'green beans')],
    'objective_function': '9*x0^2 + 9*x0*x1 + 5*x0*x2 + 5*x1^2 + 4*x1*x2 + x0 + 7*x1 + 2*x2',
    'constraints': [
        '12*x0 = 12',
        '2*x0 = 2',
        'x1 = 1',
        '4*x1 = 4',
        '13*x2 = 13',
        '12*x2 = 12',
        '12*x0 + 13*x2 >= 15',
        '12*x0 + x1 + 13*x2 >= 15',
        '4*x1 + 12*x2 >= 19',
        '2*x0 + 4*x1 >= 15',
        '2*x0 + 4*x1 + 12*x2 >= 15',
        '10*x1 - 5*x2 >= 0',
        '2*x0 + 4*x1 + 12*x2 <= 138',
        'x0 >= 0 and x0 is integer',
        'x1 >= 0 and x1 is integer',
        'x2 >= 0 and x2 is integer'
    ]
}
```

## 7: Gurobi Code
```python
import gurobi

def optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="peanutbutter_sandwiches", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="pickles", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="green_beans", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(9*x0**2 + 9*x0*x1 + 5*x0*x2 + 5*x1**2 + 4*x1*x2 + x0 + 7*x1 + 2*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(12*x0 == 12)
    model.addConstr(2*x0 == 2)
    model.addConstr(x1 == 1)
    model.addConstr(4*x1 == 4)
    model.addConstr(13*x2 == 13)
    model.addConstr(12*x2 == 12)
    model.addConstr(12*x0 + 13*x2 >= 15)
    model.addConstr(12*x0 + x1 + 13*x2 >= 15)
    model.addConstr(4*x1 + 12*x2 >= 19)
    model.addConstr(2*x0 + 4*x1 >= 15)
    model.addConstr(2*x0 + 4*x1 + 12*x2 >= 15)
    model.addConstr(10*x1 - 5*x2 >= 0)
    model.addConstr(2*x0 + 4*x1 + 12*x2 <= 138)

    # Bounds
    model.addConstr(x0 >= 0)
    model.addConstr(x1 >= 0)
    model.addConstr(x2 >= 0)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
    else:
        print("No solution found")

optimization_problem()
```