## Step 1: Define the symbolic representation of the variables
The variables are 'rotisserie chickens', 'oranges', 'blueberry pies', and 'green beans', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $6.14x_0 + 5.7x_1 + 4.56x_2 + 8.45x_3$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $21x_1 + 1x_3 \geq 49$
- $5x_0 + 34x_2 \geq 42$
- $5x_0 + 21x_1 \geq 21$
- $5x_0 + 1x_3 \geq 26$
- $12x_0 + 30x_1 \geq 83$
- $5x_0 + 21x_1 \leq 109$
- $5x_0 + 34x_2 \leq 125$
- $21x_1 + 1x_3 \leq 195$
- $5x_0 + 1x_3 \leq 206$
- $34x_2 + 1x_3 \leq 229$
- $5x_0 + 21x_1 + 34x_2 + 1x_3 \leq 229$
- $15x_2 + 13x_3 \leq 335$
- $12x_0 + 15x_2 \leq 446$
- $12x_0 + 13x_3 \leq 146$
- $12x_0 + 30x_1 \leq 226$
- $30x_1 + 15x_2 \leq 352$
- $12x_0 + 30x_1 + 15x_2 + 13x_3 \leq 352$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'rotisserie chickens'), 
        ('x1', 'oranges'), 
        ('x2', 'blueberry pies'), 
        ('x3', 'green beans')
    ], 
    'objective_function': '6.14*x0 + 5.7*x1 + 4.56*x2 + 8.45*x3', 
    'constraints': [
        '21*x1 + x3 >= 49',
        '5*x0 + 34*x2 >= 42',
        '5*x0 + 21*x1 >= 21',
        '5*x0 + x3 >= 26',
        '12*x0 + 30*x1 >= 83',
        '5*x0 + 21*x1 <= 109',
        '5*x0 + 34*x2 <= 125',
        '21*x1 + x3 <= 195',
        '5*x0 + x3 <= 206',
        '34*x2 + x3 <= 229',
        '5*x0 + 21*x1 + 34*x2 + x3 <= 229',
        '15*x2 + 13*x3 <= 335',
        '12*x0 + 15*x2 <= 446',
        '12*x0 + 13*x3 <= 146',
        '12*x0 + 30*x1 <= 226',
        '30*x1 + 15*x2 <= 352',
        '12*x0 + 30*x1 + 15*x2 + 13*x3 <= 352'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # rotisserie chickens
    x1 = model.addVar(name="x1", lb=0)  # oranges
    x2 = model.addVar(name="x2", lb=0)  # blueberry pies
    x3 = model.addVar(name="x3", lb=0)  # green beans

    # Define the objective function
    model.setObjective(6.14 * x0 + 5.7 * x1 + 4.56 * x2 + 8.45 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(21 * x1 + x3 >= 49)
    model.addConstr(5 * x0 + 34 * x2 >= 42)
    model.addConstr(5 * x0 + 21 * x1 >= 21)
    model.addConstr(5 * x0 + x3 >= 26)
    model.addConstr(12 * x0 + 30 * x1 >= 83)
    model.addConstr(5 * x0 + 21 * x1 <= 109)
    model.addConstr(5 * x0 + 34 * x2 <= 125)
    model.addConstr(21 * x1 + x3 <= 195)
    model.addConstr(5 * x0 + x3 <= 206)
    model.addConstr(34 * x2 + x3 <= 229)
    model.addConstr(5 * x0 + 21 * x1 + 34 * x2 + x3 <= 229)
    model.addConstr(15 * x2 + 13 * x3 <= 335)
    model.addConstr(12 * x0 + 15 * x2 <= 446)
    model.addConstr(12 * x0 + 13 * x3 <= 146)
    model.addConstr(12 * x0 + 30 * x1 <= 226)
    model.addConstr(30 * x1 + 15 * x2 <= 352)
    model.addConstr(12 * x0 + 30 * x1 + 15 * x2 + 13 * x3 <= 352)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x0 (rotisserie chickens): ", x0.varValue)
        print("x1 (oranges): ", x1.varValue)
        print("x2 (blueberry pies): ", x2.varValue)
        print("x3 (green beans): ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```