To solve this optimization problem, we will use the Gurobi Python library. The goal is to minimize the objective function `2*x0 + 9*x1`, where `x0` represents the amount of kale salads and `x1` represents the quantity of chicken thighs.

The constraints are as follows:
- `2*x0 + 14*x1 >= 45`: The total combined grams of protein from kale salads plus chicken thighs has to be at least 45.
- `2*x0 + 14*x1 <= 94`: The total combined grams of protein from kale salads plus chicken thighs should be at most 94.
- `7*x0 + x1 >= 22`: The total combined healthiness rating from kale salads and chicken thighs has to be at least 22.
- `7*x0 + x1 <= 55`: The total combined healthiness rating from kale salads and chicken thighs should be at most 55.
- `-10*x0 + 7*x1 >= 0`: -10 times the number of kale salads, plus 7 times the number of chicken thighs should be at least zero.

Since `x0` (kale salads) can be a non-integer value and `x1` (chicken thighs) can also be a non-integer value, we will not add any integrality constraints to these variables.

Here is the Gurobi code that represents this optimization problem:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="kale_salads")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="chicken_thighs")

# Set the objective function
m.setObjective(2*x0 + 9*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x0 + 14*x1 >= 45, "protein_min")
m.addConstr(2*x0 + 14*x1 <= 94, "protein_max")
m.addConstr(7*x0 + x1 >= 22, "healthiness_min")
m.addConstr(7*x0 + x1 <= 55, "healthiness_max")
m.addConstr(-10*x0 + 7*x1 >= 0, "other_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"kale_salads: {x0.x}")
    print(f"chicken_thighs: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```