To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the provided information.

Let's denote:
- $x_1$ as the number of bagged salads,
- $x_2$ as the number of chicken drumsticks.

The objective function to minimize is given by $1.79x_1 + 2.18x_2$.

The constraints are as follows:
1. Calcium from bagged salads and chicken drumsticks must be at least 38 milligrams: $6x_1 + 8x_2 \geq 38$.
2. At least 37 grams of carbohydrates must come from both: $7x_1 + 8x_2 \geq 37$.
3. The constraint on calcium intake from both sources is also bounded above by 59 milligrams: $6x_1 + 8x_2 \leq 59$.
4. At most 62 grams of carbohydrates can come from both: $7x_1 + 8x_2 \leq 62$.
5. A specific linear combination constraint: $8x_1 - 2x_2 \geq 0$.

Given that both $x_1$ and $x_2$ can be fractional, we do not need to impose integer constraints on them.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'bagged salads'), ('x2', 'chicken drumsticks')],
    'objective_function': '1.79*x1 + 2.18*x2',
    'constraints': [
        '6*x1 + 8*x2 >= 38',
        '7*x1 + 8*x2 >= 37',
        '6*x1 + 8*x2 <= 59',
        '7*x1 + 8*x2 <= 62',
        '8*x1 - 2*x2 >= 0'
    ]
}
```

Now, let's implement this problem in Gurobi Python:
```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="bagged_salads")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="chicken_drumsticks")

# Set the objective function
m.setObjective(1.79*x1 + 2.18*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(6*x1 + 8*x2 >= 38, "calcium_lower_bound")
m.addConstr(7*x1 + 8*x2 >= 37, "carbohydrates_lower_bound")
m.addConstr(6*x1 + 8*x2 <= 59, "calcium_upper_bound")
m.addConstr(7*x1 + 8*x2 <= 62, "carbohydrates_upper_bound")
m.addConstr(8*x1 - 2*x2 >= 0, "linear_combination_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bagged salads: {x1.x}")
    print(f"Chicken drumsticks: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```