## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'oranges' and 'bowls of cereal', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $4.58x_1 + 4.28x_2$. The constraints are:
- The sourness index of oranges is 20, and of bowls of cereal is 18.
- The total combined sourness index must be greater than or equal to 22: $20x_1 + 18x_2 \geq 22$.
- The total combined sourness index must be at most 33: $20x_1 + 18x_2 \leq 33$.
- $-3x_1 + 3x_2 \geq 0$.

## 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ for 'oranges'
- $x_2$ for 'bowls of cereal'

The objective function in symbolic notation is: $4.58x_1 + 4.28x_2$.

The constraints in symbolic notation are:
- $20x_1 + 18x_2 \geq 22$
- $20x_1 + 18x_2 \leq 33$
- $-3x_1 + 3x_2 \geq 0$

## 3: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'oranges'), ('x2', 'bowls of cereal')],
    'objective_function': '4.58*x1 + 4.28*x2',
    'constraints': [
        '20*x1 + 18*x2 >= 22',
        '20*x1 + 18*x2 <= 33',
        '-3*x1 + 3*x2 >= 0'
    ]
}
```

## 4: Implement the problem using Gurobi
We will now implement this problem using Gurobi in Python.

```python
import gurobi

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

    # Define the variables
    oranges = model.addVar(name='oranges', lb=0)  # We assume oranges can be fractional and non-negative
    bowls_of_cereal = model.addVar(name='bowls of cereal', lb=0)  # We assume bowls of cereal can be fractional and non-negative

    # Define the objective function
    model.setObjective(4.58 * oranges + 4.28 * bowls_of_cereal, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(20 * oranges + 18 * bowls_of_cereal >= 22, name='sourness_index_min')
    model.addConstr(20 * oranges + 18 * bowls_of_cereal <= 33, name='sourness_index_max')
    model.addConstr(-3 * oranges + 3 * bowls_of_cereal >= 0, name='balance_constraint')

    # Optimize the model
    model.optimize()

    # Print the status of the model
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Oranges: {oranges.varValue}')
        print(f'Bowls of cereal: {bowls_of_cereal.varValue}')
        print(f'Objective: {model.objVal}')
    elif model.status == gurobi.GRB.INFEASIBLE:
        print('The model is infeasible.')
    else:
        print('The model has a non-optimal status.')

solve_optimization_problem()
```