To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using algebraic notation.

### Symbolic Representation

- **Variables**: Let's denote 'fruit salads' as $x_1$, 'slices of pizza' as $x_2$, and 'knishes' as $x_3$.
- **Objective Function**: The goal is to maximize $9.13x_1 + 3.0x_2 + 7.04x_3$.
- **Constraints**:
  1. Fat from slices of pizza and knishes: $14x_2 + 8x_3 \geq 52$
  2. Relationship between slices of pizza and knishes: $6x_2 - 5x_3 \geq 0$
  3. Fat limit from fruit salads and knishes: $4x_1 + 8x_3 \leq 81$
  4. Fat limit from slices of pizza and knishes: $14x_2 + 8x_3 \leq 117$
  5. Total fat limit: $4x_1 + 14x_2 + 8x_3 \leq 181$

Given that the variables can be non-integer, we're dealing with a linear programming problem.

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'fruit salads'), ('x2', 'slices of pizza'), ('x3', 'knishes')],
    'objective_function': '9.13*x1 + 3.0*x2 + 7.04*x3',
    'constraints': [
        '14*x2 + 8*x3 >= 52',
        '6*x2 - 5*x3 >= 0',
        '4*x1 + 8*x3 <= 81',
        '14*x2 + 8*x3 <= 117',
        '4*x1 + 14*x2 + 8*x3 <= 181'
    ]
}
```

### Gurobi Code

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, name="fruit_salads")
x2 = m.addVar(lb=0, name="slices_of_pizza")
x3 = m.addVar(lb=0, name="knishes")

# Set the objective function
m.setObjective(9.13*x1 + 3.0*x2 + 7.04*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(14*x2 + 8*x3 >= 52, "fat_from_pizza_knishes")
m.addConstr(6*x2 - 5*x3 >= 0, "pizza_knishes_relationship")
m.addConstr(4*x1 + 8*x3 <= 81, "fat_limit_fruit_knishes")
m.addConstr(14*x2 + 8*x3 <= 117, "fat_limit_pizza_knishes")
m.addConstr(4*x1 + 14*x2 + 8*x3 <= 181, "total_fat_limit")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"fruit_salads: {x1.x}")
    print(f"slices_of_pizza: {x2.x}")
    print(f"knishes: {x3.x}")
else:
    print("No optimal solution found")
```