## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'steaks'
- $x_1$ represents 'peanutbutter sandwiches'
- $x_2$ represents 'ham sandwiches'
- $x_3$ represents 'pickles'
- $x_4$ represents 'chicken drumsticks'

## Step 2: Formulate the objective function
The objective function to maximize is $5.13x_0 + 3.46x_1 + 4.8x_2 + 8.09x_3 + 4.59x_4$.

## 3: Define the constraints
The constraints are:
- $6x_1 + 7x_2 \geq 21$ (at least $21 on peanutbutter sandwiches and ham sandwiches)
- $8x_0 + 6x_1 + 7x_2 \geq 21$ (at least $21 on steaks, peanutbutter sandwiches, and ham sandwiches)
- $-8x_1 + 3x_3 + 8x_4 \geq 0$
- $7x_2 + 9x_4 \leq 46$ (no more than $46 on ham sandwiches and chicken drumsticks)
- $8x_0 + 9x_4 \leq 28$ (no more than $28 on steaks and chicken drumsticks)
- $6x_1 + 9x_3 \leq 106$ (no more than $106 on peanutbutter sandwiches and pickles)
- $8x_0 + 6x_1 + 9x_3 \leq 110$ (no more than $110 on steaks, peanutbutter sandwiches, and pickles)
- $8x_0 + 6x_1 + 7x_2 + 9x_3 + 9x_4 \leq 141$ (no more than $141 total)
- $x_0, x_1, x_2, x_3, x_4 \geq 0$ (non-negativity constraints, though not explicitly stated, they are implied for quantities)

## 4: Correct and Complete the Formulation
Given that the cost coefficients are:
- $r_0 = [8, 6, 7, 9, 9]$ for $x_0, x_1, x_2, x_3, x_4$ respectively.

## 5: Symbolic Representation
The symbolic representation is:
```json
{
    'sym_variables': [
        ('x0', 'steaks'), 
        ('x1', 'peanutbutter sandwiches'), 
        ('x2', 'ham sandwiches'), 
        ('x3', 'pickles'), 
        ('x4', 'chicken drumsticks')
    ], 
    'objective_function': '5.13*x0 + 3.46*x1 + 4.8*x2 + 8.09*x3 + 4.59*x4', 
    'constraints': [
        '6*x1 + 7*x2 >= 21',
        '8*x0 + 6*x1 + 7*x2 >= 21',
        '-8*x1 + 3*x3 + 8*x4 >= 0',
        '7*x2 + 9*x4 <= 46',
        '8*x0 + 9*x4 <= 28',
        '6*x1 + 9*x3 <= 106',
        '8*x0 + 6*x1 + 9*x3 <= 110',
        '8*x0 + 6*x1 + 7*x2 + 9*x3 + 9*x4 <= 141'
    ]
}
```

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

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

    # Define variables
    x0 = m.addVar(name="steaks", lb=0)  # steaks
    x1 = m.addVar(name="peanutbutter_sandwiches", lb=0)  # peanutbutter sandwiches
    x2 = m.addVar(name="ham_sandwiches", lb=0)  # ham sandwiches
    x3 = m.addVar(name="pickles", lb=0)  # pickles
    x4 = m.addVar(name="chicken_drumsticks", lb=0)  # chicken drumsticks

    # Objective function
    m.setObjective(5.13*x0 + 3.46*x1 + 4.8*x2 + 8.09*x3 + 4.59*x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    m.addConstr(6*x1 + 7*x2 >= 21)  # at least $21 on peanutbutter sandwiches and ham sandwiches
    m.addConstr(8*x0 + 6*x1 + 7*x2 >= 21)  # at least $21 on steaks, peanutbutter sandwiches, and ham sandwiches
    m.addConstr(-8*x1 + 3*x3 + 8*x4 >= 0)
    m.addConstr(7*x2 + 9*x4 <= 46)  # no more than $46 on ham sandwiches and chicken drumsticks
    m.addConstr(8*x0 + 9*x4 <= 28)  # no more than $28 on steaks and chicken drumsticks
    m.addConstr(6*x1 + 9*x3 <= 106)  # no more than $106 on peanutbutter sandwiches and pickles
    m.addConstr(8*x0 + 6*x1 + 9*x3 <= 110)  # no more than $110 on steaks, peanutbutter sandwiches, and pickles
    m.addConstr(8*x0 + 6*x1 + 7*x2 + 9*x3 + 9*x4 <= 141)  # no more than $141 total

    # Optimize
    m.optimize()

    # Print solution
    if m.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", m.objVal)
        print("Steaks: ", x0.varValue)
        print("Peanutbutter sandwiches: ", x1.varValue)
        print("Ham sandwiches: ", x2.varValue)
        print("Pickles: ", x3.varValue)
        print("Chicken drumsticks: ", x4.varValue)
    else:
        print("The problem is infeasible")

optimization_problem()
```