## Step 1: Define the symbolic representation of the variables
The variables are 'knishes', 'granola bars', and 'fruit salads', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the problem description into a symbolic representation
The objective function to maximize is $1x_1 + 1x_2 + 2x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $16x_1 + 3x_2 + 19x_3 \leq 69$ (total carbohydrates constraint)
- $16x_1 + 19x_3 \geq 20$ (carbohydrates from knishes and fruit salads)
- $3x_2 + 19x_3 \geq 15$ (carbohydrates from granola bars and fruit salads)
- $16x_1 + 19x_3 \leq 34$ (upper bound from knishes and fruit salads)
- $3x_2 + 19x_3 \leq 64$ (upper bound from granola bars and fruit salads)
- $16x_1 + 3x_2 + 19x_3 \leq 64$ (upper bound from all)

## 4: Define the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'knishes'), ('x2', 'granola bars'), ('x3', 'fruit salads')],
'objective_function': '1*x1 + 1*x2 + 2*x3',
'constraints': [
    '16*x1 + 3*x2 + 19*x3 <= 69',
    '16*x1 + 19*x3 >= 20',
    '3*x2 + 19*x3 >= 15',
    '16*x1 + 19*x3 <= 34',
    '3*x2 + 19*x3 <= 64',
    '16*x1 + 3*x2 + 19*x3 <= 64'
]
}
```

## 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    knishes = model.addVar(name="knishes", lb=0)
    granola_bars = model.addVar(name="granola_bars", lb=0)
    fruit_salads = model.addVar(name="fruit_salads", lb=0)

    # Define the objective function
    model.setObjective(knishes + granola_bars + 2 * fruit_salads, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(16 * knishes + 3 * granola_bars + 19 * fruit_salads <= 69)
    model.addConstr(16 * knishes + 19 * fruit_salads >= 20)
    model.addConstr(3 * granola_bars + 19 * fruit_salads >= 15)
    model.addConstr(16 * knishes + 19 * fruit_salads <= 34)
    model.addConstr(3 * granola_bars + 19 * fruit_salads <= 64)
    model.addConstr(16 * knishes + 3 * granola_bars + 19 * fruit_salads <= 64)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Knishes: {knishes.varValue}")
        print(f"Granola bars: {granola_bars.varValue}")
        print(f"Fruit salads: {fruit_salads.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```