To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (kale salads and chicken thighs), expressing the objective function in terms of these variables, and listing all constraints using these symbolic variables.

### Symbolic Representation

Let's denote:
- \(x_1\) as the amount of kale salads,
- \(x_2\) as the quantity of chicken thighs.

The objective function to minimize is: \(2x_1 + 9x_2\).

Constraints are as follows:
1. Total grams of protein from kale salads and chicken thighs must be at least 45: \(2x_1 + 14x_2 \geq 45\).
2. The total combined healthiness rating from both items should be no less than 22: \(7x_1 + x_2 \geq 22\).
3. \(-10x_1 + 7x_2 \geq 0\).
4. Total grams of protein must not exceed 94: \(2x_1 + 14x_2 \leq 94\).
5. The total combined healthiness rating should be at most 55: \(7x_1 + x_2 \leq 55\).

The symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'kale salads'), ('x2', 'chicken thighs')],
    'objective_function': '2*x1 + 9*x2',
    'constraints': [
        '2*x1 + 14*x2 >= 45',
        '7*x1 + x2 >= 22',
        '-10*x1 + 7*x2 >= 0',
        '2*x1 + 14*x2 <= 94',
        '7*x1 + x2 <= 55'
    ]
}
```

### Gurobi Code

Now, let's implement this optimization problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="kale_salads")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="chicken_thighs")

# Set the objective function
m.setObjective(2*x1 + 9*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 14*x2 >= 45, "protein_min")
m.addConstr(7*x1 + x2 >= 22, "healthiness_min")
m.addConstr(-10*x1 + 7*x2 >= 0, "custom_constraint")
m.addConstr(2*x1 + 14*x2 <= 94, "protein_max")
m.addConstr(7*x1 + x2 <= 55, "healthiness_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Kale Salads: {x1.x}")
    print(f"Chicken Thighs: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```