## Step 1: Define the symbolic representation of the variables
The variables are 'kale salads' and 'chicken thighs', which we can represent symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : 'kale salads'
- $x_2$ : 'chicken thighs'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $2x_1 + 9x_2$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $2x_1 + 14x_2 \geq 45$ (total combined grams of protein)
- $2x_1 + 14x_2 \leq 94$ (total combined grams of protein upper bound, corrected from 98 to 94 as per problem seems to have a typo and 98 was mentioned as an upper bound for r0 but 94 is used)
- $7x_1 + x_2 \geq 22$ (total combined healthiness rating)
- $7x_1 + x_2 \leq 55$ (total combined healthiness rating upper bound)
- $-10x_1 + 7x_2 \geq 0$ (linear combination of $x_1$ and $x_2$)

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'kale salads'), ('x2', 'chicken thighs')],
'objective_function': '2*x1 + 9*x2',
'constraints': [
    '2*x1 + 14*x2 >= 45',
    '2*x1 + 14*x2 <= 94',
    '7*x1 + x2 >= 22',
    '7*x1 + x2 <= 55',
    '-10*x1 + 7*x2 >= 0'
]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='kale_salads', lb=0)  # Assuming non-negative
    x2 = model.addVar(name='chicken_thighs', lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(2*x1 + 9*x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(2*x1 + 14*x2 >= 45, name='protein_min')
    model.addConstr(2*x1 + 14*x2 <= 94, name='protein_max')
    model.addConstr(7*x1 + x2 >= 22, name='healthiness_min')
    model.addConstr(7*x1 + x2 <= 55, name='healthiness_max')
    model.addConstr(-10*x1 + 7*x2 >= 0, name='linear_combination')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Kale Salads: ', x1.varValue)
        print('Chicken Thighs: ', x2.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```