## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- 'x1' corresponds to 'strawberries'
- 'x2' corresponds to 'bowls of cereal'
- 'x3' corresponds to 'kale salads'
- 'x4' corresponds to 'potatoes'

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is: $4.13x_1 + 9.93x_2 + 2.98x_3 + 1.85x_4$

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $3.63x_1 \geq 21$ (with $x_3$ for strawberries and kale salads, but correctly it should involve $x_1$ and $x_3$)
- $7.08x_2 + 8.36x_3 \geq 14$ (for bowls of cereal and kale salads)
- $7.08x_2 + 8.36x_3 + 2.7x_4 \geq 22$ (for bowls of cereal, kale salads, and potatoes)
- $3.63x_1 + 7.08x_2 + 2.7x_4 \geq 22$ (for strawberries, bowls of cereal, and potatoes)
- $3.63x_1 + 7.08x_2 + 8.36x_3 \geq 22$ (for strawberries, bowls of cereal, and kale salads, but note repetition with previous)
- $3.63x_1 + 7.08x_2 + 2.7x_4 \geq 22$ (same as one above)
- $3.63x_1 + 7.08x_2 + 8.36x_3 + 2.7x_4 \geq 22$ (for all)
- $-10x_1 + 2x_2 \geq 0$
- $-3x_1 + 10x_4 \geq 0$
- $3.63x_1 + 7.08x_2 \leq 121$
- $7.08x_2 + 8.36x_3 + 2.7x_4 \leq 69$
- $3.63x_1 + 7.08x_2 + 8.36x_3 \leq 42$
- $3.63x_1 + 7.08x_2 + 2.7x_4 \leq 53$
- $0 \leq x_1$
- $0 \leq x_2$
- $0 \leq x_3$
- $0 \leq x_4$

Correcting and properly accounting for fiber sources:
- From strawberries and kale salads: $3.63x_1 + 8.36x_3 \geq 21$
- The rest are properly identified.

## 4: Write down the problem in a structured format
```json
{
    'sym_variables': [('x1', 'strawberries'), ('x2', 'bowls of cereal'), ('x3', 'kale salads'), ('x4', 'potatoes')],
    'objective_function': '4.13x1 + 9.93x2 + 2.98x3 + 1.85x4',
    'constraints': [
        '3.63x1 + 8.36x3 >= 21',
        '7.08x2 + 8.36x3 >= 14',
        '7.08x2 + 8.36x3 + 2.7x4 >= 22',
        '3.63x1 + 7.08x2 + 2.7x4 >= 22',
        '3.63x1 + 7.08x2 + 8.36x3 >= 22',
        '3.63x1 + 7.08x2 + 8.36x3 + 2.7x4 >= 22',
        '-10x1 + 2x2 >= 0',
        '-3x1 + 10x4 >= 0',
        '3.63x1 + 7.08x2 <= 121',
        '7.08x2 + 8.36x3 + 2.7x4 <= 69',
        '3.63x1 + 7.08x2 + 8.36x3 <= 42',
        '3.63x1 + 7.08x2 + 2.7x4 <= 53',
        'x1 >= 0',
        'x2 >= 0',
        'x3 >= 0',
        'x4 >= 0',
        '3.63x1 <= 136',
        '7.08x2 <= 136',
        '8.36x3 <= 136',
        '2.7x4 <= 136'
    ]
}
```

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

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="strawberries", lb=0)
    x2 = model.addVar(name="bowls of cereal", lb=0)
    x3 = model.addVar(name="kale salads", lb=0)
    x4 = model.addVar(name="potatoes", lb=0)

    # Objective function
    model.setObjective(4.13 * x1 + 9.93 * x2 + 2.98 * x3 + 1.85 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(3.63 * x1 + 8.36 * x3 >= 21)
    model.addConstr(7.08 * x2 + 8.36 * x3 >= 14)
    model.addConstr(7.08 * x2 + 8.36 * x3 + 2.7 * x4 >= 22)
    model.addConstr(3.63 * x1 + 7.08 * x2 + 2.7 * x4 >= 22)
    model.addConstr(3.63 * x1 + 7.08 * x2 + 8.36 * x3 >= 22)
    model.addConstr(3.63 * x1 + 7.08 * x2 + 8.36 * x3 + 2.7 * x4 >= 22)
    model.addConstr(-10 * x1 + 2 * x2 >= 0)
    model.addConstr(-3 * x1 + 10 * x4 >= 0)
    model.addConstr(3.63 * x1 + 7.08 * x2 <= 121)
    model.addConstr(7.08 * x2 + 8.36 * x3 + 2.7 * x4 <= 69)
    model.addConstr(3.63 * x1 + 7.08 * x2 + 8.36 * x3 <= 42)
    model.addConstr(3.63 * x1 + 7.08 * x2 + 2.7 * x4 <= 53)
    model.addConstr(3.63 * x1 <= 136)
    model.addConstr(7.08 * x2 <= 136)
    model.addConstr(8.36 * x3 <= 136)
    model.addConstr(2.7 * x4 <= 136)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Strawberries: {x1.varValue}")
        print(f"Bowls of cereal: {x2.varValue}")
        print(f"Kale salads: {x3.varValue}")
        print(f"Potatoes: {x4.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```