To solve the optimization problem described, we'll formulate it as a linear programming problem. The objective is to minimize the function `4.58 * oranges + 4.28 * bowls_of_cereal` under the given constraints.

Let's denote:
- `oranges` as the amount of oranges,
- `bowls_of_cereal` as the number of bowls of cereal.

Given constraints are:
1. The sourness index of oranges is 20, and of bowls of cereal is 18.
2. The total combined sourness index from both must be at least 22: `20 * oranges + 18 * bowls_of_cereal >= 22`.
3. The constraint about the total combined sourness index being greater than or equal to 22 is repeated, so we consider it as one constraint.
4. `-3 * oranges + 3 * bowls_of_cereal >= 0`, which simplifies to `oranges <= bowls_of_cereal`.
5. The total combined sourness index must be at most 33: `20 * oranges + 18 * bowls_of_cereal <= 33`.

Since fractional amounts of both variables are allowed, we don't need to worry about integer constraints.

Here's how we can represent this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define the variables
oranges = m.addVar(vtype=GRB.CONTINUOUS, name="oranges")
bowls_of_cereal = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_cereal")

# Set the objective function
m.setObjective(4.58 * oranges + 4.28 * bowls_of_cereal, GRB.MINIMIZE)

# Add constraints
m.addConstr(20 * oranges + 18 * bowls_of_cereal >= 22, "Total_Sourness_Min")
m.addConstr(oranges <= bowls_of_cereal, "Oranges_vs_Bowls")
m.addConstr(20 * oranges + 18 * bowls_of_cereal <= 33, "Total_Sourness_Max")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Oranges: {oranges.x}")
    print(f"Bowls of Cereal: {bowls_of_cereal.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found.")
```