## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'slices of pizza', and 'knishes', which we will denote as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $9.13x_1 + 3.0x_2 + 7.04x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $4x_1 + 14x_2 + 8x_3 \leq 181$ (total fat constraint)
2. $14x_2 + 8x_3 \geq 52$ (minimum fat from pizza and knishes)
3. $6x_2 - 5x_3 \geq 0$ (relationship between pizza and knishes)
4. $4x_1 + 8x_3 \leq 81$ (fat from fruit salads and knishes)
5. $14x_2 + 8x_3 \leq 117$ (fat from pizza and knishes)
6. $4x_1 + 14x_2 + 8x_3 \leq 117$ (total fat from all sources)

## 4: Determine the symbolic representation of the problem
The symbolic representation is:
- Variables: $(x_1, 'fruit salads'), (x_2, 'slices of pizza'), (x_3, 'knishes')$
- Objective function: $9.13x_1 + 3.0x_2 + 7.04x_3$
- Constraints:
1. $4x_1 + 14x_2 + 8x_3 \leq 181$
2. $14x_2 + 8x_3 \geq 52$
3. $6x_2 - 5x_3 \geq 0$
4. $4x_1 + 8x_3 \leq 81$
5. $14x_2 + 8x_3 \leq 117$
6. $4x_1 + 14x_2 + 8x_3 \leq 117$

## 5: Implement the problem in Gurobi
We will now implement this problem using Gurobi.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="fruit_salads", lb=0)  # No lower bound specified, assuming 0
x2 = model.addVar(name="slices_of_pizza", lb=0)  # No lower bound specified, assuming 0
x3 = model.addVar(name="knishes", lb=0)  # No lower bound specified, assuming 0

# Objective function
model.setObjective(9.13 * x1 + 3.0 * x2 + 7.04 * x3, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(4 * x1 + 14 * x2 + 8 * x3 <= 181, name="total_fat")
model.addConstr(14 * x2 + 8 * x3 >= 52, name="min_fat_pizza_knishes")
model.addConstr(6 * x2 - 5 * x3 >= 0, name="pizza_knishes_relation")
model.addConstr(4 * x1 + 8 * x3 <= 81, name="fat_fruit_salads_knishes")
model.addConstr(14 * x2 + 8 * x3 <= 117, name="fat_pizza_knishes")
model.addConstr(4 * x1 + 14 * x2 + 8 * x3 <= 117, name="total_fat_limited")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Fruit Salads: ", x1.varValue)
    print("Slices of Pizza: ", x2.varValue)
    print("Knishes: ", x3.varValue)
else:
    print("The model is infeasible")
```

## 6: Provide the final output in the required 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': [
        '4*x1 + 14*x2 + 8*x3 <= 181',
        '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 <= 117'
    ]
}
```

```python
import gurobi

# Create a new model
model = gurobi.Model()

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

# Objective function
model.setObjective(9.13 * x1 + 3.0 * x2 + 7.04 * x3, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(4 * x1 + 14 * x2 + 8 * x3 <= 181, name="total_fat")
model.addConstr(14 * x2 + 8 * x3 >= 52, name="min_fat_pizza_knishes")
model.addConstr(6 * x2 - 5 * x3 >= 0, name="pizza_knishes_relation")
model.addConstr(4 * x1 + 8 * x3 <= 81, name="fat_fruit_salads_knishes")
model.addConstr(14 * x2 + 8 * x3 <= 117, name="fat_pizza_knishes")
model.addConstr(4 * x1 + 14 * x2 + 8 * x3 <= 117, name="total_fat_limited")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Fruit Salads: ", x1.varValue)
    print("Slices of Pizza: ", x2.varValue)
    print("Knishes: ", x3.varValue)
else:
    print("The model is infeasible")
```