## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'bananas' and 'apples', which can be represented symbolically as $x_1$ and $x_2$ respectively. The objective function to maximize is $6.16x_1 + 1.89x_2$. The constraints are:
- $3x_1 + 4x_2 \geq 28$ (minimum spend of $28)
- $2x_1 + x_2 \geq 12$ (minimum fiber of 12 grams)
- $10x_1 - 4x_2 \geq 0$ (relationship between bananas and apples)
- $3x_1 + 4x_2 \leq 41$ (maximum spend of $41)
- $2x_1 + x_2 \leq 29$ (maximum fiber of 29 grams)
- $x_1, x_2 \geq 0$ (non-negativity, but since the problem mentions that there does not have to be a whole number, $x_1, x_2$ can be continuous)

## 2: Convert the problem into a Gurobi-compatible format
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'bananas'), ('x2', 'apples')],
'objective_function': '6.16*x1 + 1.89*x2',
'constraints': [
    '3*x1 + 4*x2 >= 28',
    '2*x1 + x2 >= 12',
    '10*x1 - 4*x2 >= 0',
    '3*x1 + 4*x2 <= 41',
    '2*x1 + x2 <= 29'
]
}
```

## 3: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    bananas = model.addVar(name="bananas", lb=0)  # No upper bound, can be continuous
    apples = model.addVar(name="apples", lb=0)   # No upper bound, can be continuous

    # Define the objective function
    model.setObjective(6.16 * bananas + 1.89 * apples, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * bananas + 4 * apples >= 28)  # Minimum spend $28
    model.addConstr(2 * bananas + apples >= 12)     # Minimum fiber 12 grams
    model.addConstr(10 * bananas - 4 * apples >= 0) # Relationship between bananas and apples
    model.addConstr(3 * bananas + 4 * apples <= 41) # Maximum spend $41
    model.addConstr(2 * bananas + apples <= 29)     # Maximum fiber 29 grams

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Bananas: {bananas.varValue}")
        print(f"Apples: {apples.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```