## Step 1: Define the symbolic representation of the variables
The variables are 'corn cobs', 'strawberries', and 'milkshakes', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $9x_1 + 7x_2 + 1x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- The healthiness rating of corn cobs is 0.03$x_1$.
- The healthiness rating of strawberries is 0.93$x_2$.
- The healthiness rating of milkshakes is 0.14$x_3$.
- $0.03x_1 + 0.14x_3 \geq 47$
- $0.93x_2 + 0.14x_3 \geq 61$
- $0.03x_1 + 0.93x_2 \geq 92$
- $0.03x_1 + 0.93x_2 + 0.14x_3 \geq 79$
- $0.93x_2 + 0.14x_3 \leq 185$
- $0.03x_1 + 0.14x_3 \leq 145$
- $0.03x_1 + 0.93x_2 \leq 163$
- $0.03x_1 + 0.93x_2 + 0.14x_3 \leq 163$

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'corn cobs'), ('x2', 'strawberries'), ('x3', 'milkshakes')],
'objective_function': '9*x1 + 7*x2 + 1*x3',
'constraints': [
    '0.03*x1 + 0.14*x3 >= 47',
    '0.93*x2 + 0.14*x3 >= 61',
    '0.03*x1 + 0.93*x2 >= 92',
    '0.03*x1 + 0.93*x2 + 0.14*x3 >= 79',
    '0.93*x2 + 0.14*x3 <= 185',
    '0.03*x1 + 0.14*x3 <= 145',
    '0.03*x1 + 0.93*x2 <= 163',
    '0.03*x1 + 0.93*x2 + 0.14*x3 <= 163'
]
}
```

## 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="corn_cobs", lb=0)  # No upper bound given
    x2 = model.addVar(name="strawberries", lb=0)  # No upper bound given
    x3 = model.addVar(name="milkshakes", lb=0)  # No upper bound given

    # Define the objective function
    model.setObjective(9 * x1 + 7 * x2 + x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.03 * x1 + 0.14 * x3 >= 47)
    model.addConstr(0.93 * x2 + 0.14 * x3 >= 61)
    model.addConstr(0.03 * x1 + 0.93 * x2 >= 92)
    model.addConstr(0.03 * x1 + 0.93 * x2 + 0.14 * x3 >= 79)
    model.addConstr(0.93 * x2 + 0.14 * x3 <= 185)
    model.addConstr(0.03 * x1 + 0.14 * x3 <= 145)
    model.addConstr(0.03 * x1 + 0.93 * x2 <= 163)
    model.addConstr(0.03 * x1 + 0.93 * x2 + 0.14 * x3 <= 163)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Corn cobs: {x1.varValue}")
        print(f"Strawberries: {x2.varValue}")
        print(f"Milkshakes: {x3.varValue}")
        print(f"Objective: {model.objVal}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("No feasible solution exists.")
    else:
        print("Optimization failed.")

solve_optimization_problem()
```