## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and the objective function based on the given problem description.

## Step 2: Identify the variables and their corresponding symbolic representations
The variables are 'oreos', 'kiwis', and 'slices of pizza', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## 3: Formulate the objective function
The objective function to minimize is $8x_0 + 8x_1 + 7x_2$.

## 4: List the constraints
The constraints given are:
1. $x_0 + 23x_1 + 12x_2$ has a fiber upper bound, but specific constraints are:
   - $x_0 + 12x_2 \geq 61$
   - $x_0 + 23x_1 \geq 90$
   - $x_0 + 23x_1 + 12x_2 \geq 74$
   - This repeats the previous constraint, so we consider $x_0 + 23x_1 + 12x_2 \geq 74$ as redundant.
2. $22x_0 + 7x_1 + 6x_2$ has a sourness index upper bound and lower bounds:
   - $7x_1 + 6x_2 \geq 59$
   - $22x_0 + 7x_1 + 6x_2 \geq 59$
3. Additional constraints:
   - $-8x_1 + 7x_2 \geq 0$
   - $3x_0 - 4x_1 \geq 0$
   - $-4x_0 + 5x_2 \geq 0$
4. Fiber bounds:
   - $23x_1 + 12x_2 \leq 170$
   - $x_0 + 23x_1 \leq 358$
5. Sourness index bounds:
   - $22x_0 + 6x_2 \leq 115$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'oreos'), ('x1', 'kiwis'), ('x2', 'slices of pizza')],
    'objective_function': '8*x0 + 8*x1 + 7*x2',
    'constraints': [
        'x0 + 12*x2 >= 61',
        'x0 + 23*x1 >= 90',
        'x0 + 23*x1 + 12*x2 >= 74',
        '7*x1 + 6*x2 >= 59',
        '22*x0 + 7*x1 + 6*x2 >= 59',
        '-8*x1 + 7*x2 >= 0',
        '3*x0 - 4*x1 >= 0',
        '-4*x0 + 5*x2 >= 0',
        '23*x1 + 12*x2 <= 170',
        'x0 + 23*x1 <= 358',
        '22*x0 + 6*x2 <= 115',
        'x0 >= 0',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="oreos", lb=0)
    x1 = model.addVar(name="kiwis", lb=0)
    x2 = model.addVar(name="slices of pizza", lb=0)

    # Define the objective function
    model.setObjective(8*x0 + 8*x1 + 7*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(x0 + 12*x2 >= 61)
    model.addConstr(x0 + 23*x1 >= 90)
    model.addConstr(x0 + 23*x1 + 12*x2 >= 74)
    model.addConstr(7*x1 + 6*x2 >= 59)
    model.addConstr(22*x0 + 7*x1 + 6*x2 >= 59)
    model.addConstr(-8*x1 + 7*x2 >= 0)
    model.addConstr(3*x0 - 4*x1 >= 0)
    model.addConstr(-4*x0 + 5*x2 >= 0)
    model.addConstr(23*x1 + 12*x2 <= 170)
    model.addConstr(x0 + 23*x1 <= 358)
    model.addConstr(22*x0 + 6*x2 <= 115)

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Oreos: {x0.varValue}")
        print(f"Kiwis: {x1.varValue}")
        print(f"Slices of pizza: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```