## Step 1: Define the symbolic representation of the variables
The variables are 'bagged salads', 'chicken breasts', 'strawberries', 'kale salads', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9.24x_0 + 9.78x_1 + 9.68x_2 + 3.69x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $9x_0 + 14x_1 + 19x_2 + 17x_3 \leq 225$ (total carbohydrates constraint)
2. $9x_0 + 17x_3 \geq 26$ (carbohydrates from bagged salads and kale salads)
3. $14x_1 + 17x_3 \geq 53$ (carbohydrates from chicken breasts and kale salads)
4. $9x_0 + 14x_1 + 19x_2 + 17x_3 \geq 53$ (total carbohydrates from all sources)
5. $10x_0 - 4x_1 \geq 0$ (relationship between bagged salads and chicken breasts)
6. $14x_1 + 19x_2 \leq 221$ (carbohydrates from chicken breasts and strawberries)
7. $9x_0 + 17x_3 \leq 117$ (carbohydrates from bagged salads and kale salads)
8. $9x_0 + 14x_1 \leq 106$ (carbohydrates from bagged salads and chicken breasts)
9. $19x_2 + 17x_3 \leq 204$ (carbohydrates from strawberries and kale salads)

## 4: Create the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'bagged salads'), 
    ('x1', 'chicken breasts'), 
    ('x2', 'strawberries'), 
    ('x3', 'kale salads')
],
'objective_function': '9.24*x0 + 9.78*x1 + 9.68*x2 + 3.69*x3',
'constraints': [
    '9*x0 + 14*x1 + 19*x2 + 17*x3 <= 225',
    '9*x0 + 17*x3 >= 26',
    '14*x1 + 17*x3 >= 53',
    '9*x0 + 14*x1 + 19*x2 + 17*x3 >= 53',
    '10*x0 - 4*x1 >= 0',
    '14*x1 + 19*x2 <= 221',
    '9*x0 + 17*x3 <= 117',
    '9*x0 + 14*x1 <= 106',
    '19*x2 + 17*x3 <= 204'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="bagged_salads", lb=0)  # bagged salads
    x1 = model.addVar(name="chicken_breasts", lb=0)  # chicken breasts
    x2 = model.addVar(name="strawberries", lb=0)  # strawberries
    x3 = model.addVar(name="kale_salads", lb=0)  # kale salads

    # Objective function
    model.setObjective(9.24*x0 + 9.78*x1 + 9.68*x2 + 3.69*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(9*x0 + 14*x1 + 19*x2 + 17*x3 <= 225)  # total carbohydrates
    model.addConstr(9*x0 + 17*x3 >= 26)  # carbohydrates from bagged salads and kale salads
    model.addConstr(14*x1 + 17*x3 >= 53)  # carbohydrates from chicken breasts and kale salads
    model.addConstr(9*x0 + 14*x1 + 19*x2 + 17*x3 >= 53)  # total carbohydrates from all sources
    model.addConstr(10*x0 - 4*x1 >= 0)  # relationship between bagged salads and chicken breasts
    model.addConstr(14*x1 + 19*x2 <= 221)  # carbohydrates from chicken breasts and strawberries
    model.addConstr(9*x0 + 17*x3 <= 117)  # carbohydrates from bagged salads and kale salads
    model.addConstr(9*x0 + 14*x1 <= 106)  # carbohydrates from bagged salads and chicken breasts
    model.addConstr(19*x2 + 17*x3 <= 204)  # carbohydrates from strawberries and kale salads

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("bagged salads: ", x0.varValue)
        print("chicken breasts: ", x1.varValue)
        print("strawberries: ", x2.varValue)
        print("kale salads: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```