## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'protein bars', and 'lemons', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : fruit salads
- $x_1$ : protein bars
- $x_2$ : lemons

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $9.34x_0 + 4.2x_1 + 4.27x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $6x_0 + 6x_1 + 7x_2 \geq 26$
- $18x_0 + 18x_1 + 20x_2 \geq 29$
- $6x_0 + 6x_1 \leq 64$
- $6x_0 + 7x_2 \leq 98$
- $6x_0 + 6x_1 + 7x_2 \leq 98$
- $18x_0 + 18x_1 \leq 60$
- $18x_0 + 20x_2 \leq 46$
- $18x_0 + 18x_1 + 20x_2 \leq 46$
- $9x_0 + 17x_1 \leq 155$
- $9x_0 + 8x_2 \leq 192$
- $9x_0 + 17x_1 + 8x_2 \leq 192$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'fruit salads'), 
        ('x1', 'protein bars'), 
        ('x2', 'lemons')
    ], 
    'objective_function': '9.34*x0 + 4.2*x1 + 4.27*x2', 
    'constraints': [
        '6*x0 + 6*x1 + 7*x2 >= 26',
        '18*x0 + 18*x1 + 20*x2 >= 29',
        '6*x0 + 6*x1 <= 64',
        '6*x0 + 7*x2 <= 98',
        '6*x0 + 6*x1 + 7*x2 <= 98',
        '18*x0 + 18*x1 <= 60',
        '18*x0 + 20*x2 <= 46',
        '18*x0 + 18*x1 + 20*x2 <= 46',
        '9*x0 + 17*x1 <= 155',
        '9*x0 + 8*x2 <= 192',
        '9*x0 + 17*x1 + 8*x2 <= 192'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="fruit_salads", lb=0)  # No lower bound specified, assuming 0
    x1 = model.addVar(name="protein_bars", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="lemons", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(9.34 * x0 + 4.2 * x1 + 4.27 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(6 * x0 + 6 * x1 + 7 * x2 >= 26)
    model.addConstr(18 * x0 + 18 * x1 + 20 * x2 >= 29)
    model.addConstr(6 * x0 + 6 * x1 <= 64)
    model.addConstr(6 * x0 + 7 * x2 <= 98)
    model.addConstr(6 * x0 + 6 * x1 + 7 * x2 <= 98)
    model.addConstr(18 * x0 + 18 * x1 <= 60)
    model.addConstr(18 * x0 + 20 * x2 <= 46)
    model.addConstr(18 * x0 + 18 * x1 + 20 * x2 <= 46)
    model.addConstr(9 * x0 + 17 * x1 <= 155)
    model.addConstr(9 * x0 + 8 * x2 <= 192)
    model.addConstr(9 * x0 + 17 * x1 + 8 * x2 <= 192)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Fruit Salads: ", x0.varValue)
        print("Protein Bars: ", x1.varValue)
        print("Lemons: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```