## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- 'x1' : 'fruit salads'
- 'x2' : 'tomatoes'
- 'x3' : 'kale salads'
- 'x4' : 'oreos'
- 'x5' : 'milkshakes'

## Step 2: Formulate the objective function
The objective function to minimize is: $7x_1 + 1x_2 + 4x_3 + 4x_4 + 9x_5$

## Step 3: List all the constraints
The constraints are:
- $1x_2 + 2x_3 \geq 14$
- $4x_1 + 1x_2 \geq 30$
- $2x_3 + 7x_4 \geq 15$
- $2x_3 + 6x_5 \geq 26$
- $1x_2 + 2x_3 + 6x_5 \geq 32$
- $1x_2 + 2x_3 + 7x_4 \geq 32$
- $4x_1 + 2x_3 + 6x_5 \geq 32$
- $1x_2 + 2x_3 + 6x_5 \geq 33$
- $1x_2 + 2x_3 + 7x_4 \geq 33$
- $4x_1 + 2x_3 + 6x_5 \geq 33$
- $1x_2 + 2x_3 + 6x_5 \geq 23$
- $1x_2 + 2x_3 + 7x_4 \geq 23$
- $4x_1 + 2x_3 + 6x_5 \geq 23$
- $4x_1 + 1x_2 + 2x_3 + 7x_4 + 6x_5 \geq 23$
- $3x_1 - 7x_4 \geq 0$
- $4x_1 + 2x_3 \leq 48$
- $1x_2 + 7x_4 + 6x_5 \leq 105$
- $4x_1 + 1x_2 + 2x_3 \leq 55$
- $1x_2 + 2x_3 + 6x_5 \leq 47$
- $0 \leq 4x_1 \leq 198$ 
- $0 \leq 1x_2 \leq 198$ 
- $0 \leq 2x_3 \leq 198$ 
- $0 \leq 7x_4 \leq 198$ 
- $0 \leq 6x_5 \leq 198$ 

However, we also have specific upper bounds for calcium from the resource attributes:
- $4x_1 + 1x_2 + 2x_3 + 7x_4 + 6x_5 \leq 198$

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'fruit salads'),
        ('x2', 'tomatoes'),
        ('x3', 'kale salads'),
        ('x4', 'oreos'),
        ('x5', 'milkshakes')
    ],
    'objective_function': '7*x1 + 1*x2 + 4*x3 + 4*x4 + 9*x5',
    'constraints': [
        '1*x2 + 2*x3 >= 14',
        '4*x1 + 1*x2 >= 30',
        '2*x3 + 7*x4 >= 15',
        '2*x3 + 6*x5 >= 26',
        '1*x2 + 2*x3 + 6*x5 >= 32',
        '1*x2 + 2*x3 + 7*x4 >= 32',
        '4*x1 + 2*x3 + 6*x5 >= 32',
        '1*x2 + 2*x3 + 6*x5 >= 33',
        '1*x2 + 2*x3 + 7*x4 >= 33',
        '4*x1 + 2*x3 + 6*x5 >= 33',
        '1*x2 + 2*x3 + 6*x5 >= 23',
        '1*x2 + 2*x3 + 7*x4 >= 23',
        '4*x1 + 2*x3 + 6*x5 >= 23',
        '4*x1 + 1*x2 + 2*x3 + 7*x4 + 6*x5 >= 23',
        '3*x1 - 7*x4 >= 0',
        '4*x1 + 2*x3 <= 48',
        '1*x2 + 7*x4 + 6*x5 <= 105',
        '4*x1 + 1*x2 + 2*x3 <= 55',
        '1*x2 + 2*x3 + 6*x5 <= 47',
        '4*x1 <= 198',
        '1*x2 <= 198',
        '2*x3 <= 198',
        '7*x4 <= 198',
        '6*x5 <= 198',
        '4*x1 + 1*x2 + 2*x3 + 7*x4 + 6*x5 <= 198'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="fruit_salads", lb=0)
    x2 = model.addVar(name="tomatoes", lb=0)
    x3 = model.addVar(name="kale_salads", lb=0)
    x4 = model.addVar(name="oreos", lb=0)
    x5 = model.addVar(name="milkshakes", lb=0)

    # Objective function
    model.setObjective(7*x1 + 1*x2 + 4*x3 + 4*x4 + 9*x5, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(1*x2 + 2*x3 >= 14)
    model.addConstr(4*x1 + 1*x2 >= 30)
    model.addConstr(2*x3 + 7*x4 >= 15)
    model.addConstr(2*x3 + 6*x5 >= 26)
    model.addConstr(1*x2 + 2*x3 + 6*x5 >= 32)
    model.addConstr(1*x2 + 2*x3 + 7*x4 >= 32)
    model.addConstr(4*x1 + 2*x3 + 6*x5 >= 32)
    model.addConstr(1*x2 + 2*x3 + 6*x5 >= 33)
    model.addConstr(1*x2 + 2*x3 + 7*x4 >= 33)
    model.addConstr(4*x1 + 2*x3 + 6*x5 >= 33)
    model.addConstr(1*x2 + 2*x3 + 6*x5 >= 23)
    model.addConstr(1*x2 + 2*x3 + 7*x4 >= 23)
    model.addConstr(4*x1 + 2*x3 + 6*x5 >= 23)
    model.addConstr(4*x1 + 1*x2 + 2*x3 + 7*x4 + 6*x5 >= 23)
    model.addConstr(3*x1 - 7*x4 >= 0)
    model.addConstr(4*x1 + 2*x3 <= 48)
    model.addConstr(1*x2 + 7*x4 + 6*x5 <= 105)
    model.addConstr(4*x1 + 1*x2 + 2*x3 <= 55)
    model.addConstr(1*x2 + 2*x3 + 6*x5 <= 47)
    model.addConstr(4*x1 <= 198)
    model.addConstr(1*x2 <= 198)
    model.addConstr(2*x3 <= 198)
    model.addConstr(7*x4 <= 198)
    model.addConstr(6*x5 <= 198)
    model.addConstr(4*x1 + 1*x2 + 2*x3 + 7*x4 + 6*x5 <= 198)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print("Objective: ", model.objVal)
        print("Fruit Salads: ", x1.varValue)
        print("Tomatoes: ", x2.varValue)
        print("Kale Salads: ", x3.varValue)
        print("Oreos: ", x4.varValue)
        print("Milkshakes: ", x5.varValue)
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```