To solve this optimization problem, we first need to identify the decision variables and the constraints. The decision variables are the quantities of 'corn cobs', 'strawberries', and 'milkshakes' that we want to determine in order to maximize our objective function.

The objective function is given as: Maximize $9 \times corn\ cobs + 7 \times strawberries + 1 \times milkshakes$.

We have several constraints based on the healthiness ratings of these items and their combinations. Let's denote 'corn cobs' as $x_0$, 'strawberries' as $x_1$, and 'milkshakes' as $x_2$. Given healthiness ratings are: 
- Corn cobs: 0.03
- Strawberries: 0.93
- Milkshakes: 0.14

The constraints can be summarized as follows:
1. $0.03x_0 + 0.14x_2 \geq 47$
2. $0.93x_1 + 0.14x_2 \geq 61$
3. $0.03x_0 + 0.93x_1 \geq 92$
4. $0.03x_0 + 0.93x_1 + 0.14x_2 \geq 79$
5. $0.93x_1 + 0.14x_2 \leq 185$
6. $0.03x_0 + 0.14x_2 \leq 145$
7. $0.03x_0 + 0.93x_1 \leq 163$
8. $0.03x_0 + 0.93x_1 + 0.14x_2 \leq 163$

All variables are allowed to be non-integer (fractional).

Here is the Gurobi code that captures this optimization problem:

```python
from gurobipy import *

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

# Create decision variables
corn_cobs = m.addVar(lb=0, name="corn_cobs", vtype=GRB.CONTINUOUS)
strawberries = m.addVar(lb=0, name="strawberries", vtype=GRB.CONTINUOUS)
milkshakes = m.addVar(lb=0, name="milkshakes", vtype=GRB.CONTINUOUS)

# Define the objective function
m.setObjective(9 * corn_cobs + 7 * strawberries + 1 * milkshakes, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.03 * corn_cobs + 0.14 * milkshakes >= 47)
m.addConstr(0.93 * strawberries + 0.14 * milkshakes >= 61)
m.addConstr(0.03 * corn_cobs + 0.93 * strawberries >= 92)
m.addConstr(0.03 * corn_cobs + 0.93 * strawberries + 0.14 * milkshakes >= 79)
m.addConstr(0.93 * strawberries + 0.14 * milkshakes <= 185)
m.addConstr(0.03 * corn_cobs + 0.14 * milkshakes <= 145)
m.addConstr(0.03 * corn_cobs + 0.93 * strawberries <= 163)
m.addConstr(0.03 * corn_cobs + 0.93 * strawberries + 0.14 * milkshakes <= 163)

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Corn Cobs: {corn_cobs.x}")
    print(f"Strawberries: {strawberries.x}")
    print(f"Milkshakes: {milkshakes.x}")
else:
    print("No optimal solution found")
```