To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (knishes, granola bars, fruit salads), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's define:
- \(x_1\) as the number of knishes,
- \(x_2\) as the number of granola bars,
- \(x_3\) as the number of fruit salads.

The objective function is to maximize: \(1 \cdot x_1 + 1 \cdot x_2 + 2 \cdot x_3\).

Constraints:
1. Carbohydrates from knishes and fruit salads: \(16x_1 + 19x_3 \geq 20\).
2. Carbohydrates from granola bars and fruit salads: \(3x_2 + 19x_3 \geq 15\).
3. Maximum carbohydrates from knishes and fruit salads: \(16x_1 + 19x_3 \leq 34\).
4. Maximum carbohydrates from granola bars and fruit salads: \(3x_2 + 19x_3 \leq 64\).
5. Total maximum carbohydrates: \(16x_1 + 3x_2 + 19x_3 \leq 69\) (note: the problem statement mentions an upper bound of 69 for 'r0', which seems to refer to the total carbohydrates from all sources, and this constraint was not explicitly listed but implied by the context).

The symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'knishes'), ('x2', 'granola bars'), ('x3', 'fruit salads')],
    'objective_function': '1*x1 + 1*x2 + 2*x3',
    'constraints': [
        '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 <= 69'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="knishes")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="granola_bars")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="fruit_salads")

# Set the objective function
m.setObjective(1*x1 + 1*x2 + 2*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(16*x1 + 19*x3 >= 20, "carb_knishes_fruit")
m.addConstr(3*x2 + 19*x3 >= 15, "carb_granola_fruit")
m.addConstr(16*x1 + 19*x3 <= 34, "max_carb_knishes_fruit")
m.addConstr(3*x2 + 19*x3 <= 64, "max_carb_granola_fruit")
m.addConstr(16*x1 + 3*x2 + 19*x3 <= 69, "total_max_carb")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
```