To solve this optimization problem, we will first define the symbolic representation of the variables and the objective function. We'll then list out all the constraints as per the given description.

### Symbolic Representation:

Let's denote:
- $x_1$ as the number of chicken thighs,
- $x_2$ as the quantity of eggs.

The objective function is to maximize: $6x_1 + 6x_2$

Constraints are based on the resources/attributes provided and the additional conditions stated in the problem description. We have constraints related to calcium content, umami index, tastiness rating, and a specific linear combination of chicken thighs and eggs.

### Constraints:

1. Calcium content from chicken thighs and eggs should be at least 88 milligrams: $19x_1 + 2x_2 \geq 88$
2. The total combined umami index should be at least 23: $14x_1 + 2x_2 \geq 23$
3. The total combined tastiness rating should be at least 77: $13x_1 + 6x_2 \geq 77$
4. A specific linear combination constraint: $-7x_1 + 10x_2 \geq 0$
5. Calcium content from chicken thighs and eggs should not exceed 137 milligrams: $19x_1 + 2x_2 \leq 137$
6. The total combined umami index should not exceed 95: $14x_1 + 2x_2 \leq 95$
7. The total combined tastiness rating should not exceed 130: $13x_1 + 6x_2 \leq 130$

Given the problem description, we can represent it symbolically as follows:

```json
{
  'sym_variables': [('x1', 'chicken thighs'), ('x2', 'eggs')],
  'objective_function': '6*x1 + 6*x2',
  'constraints': [
    '19*x1 + 2*x2 >= 88',
    '14*x1 + 2*x2 >= 23',
    '13*x1 + 6*x2 >= 77',
    '-7*x1 + 10*x2 >= 0',
    '19*x1 + 2*x2 <= 137',
    '14*x1 + 2*x2 <= 95',
    '13*x1 + 6*x2 <= 130'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(6*x1 + 6*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(19*x1 + 2*x2 >= 88, "calcium_min")
m.addConstr(14*x1 + 2*x2 >= 23, "umami_min")
m.addConstr(13*x1 + 6*x2 >= 77, "tastiness_min")
m.addConstr(-7*x1 + 10*x2 >= 0, "linear_combination")
m.addConstr(19*x1 + 2*x2 <= 137, "calcium_max")
m.addConstr(14*x1 + 2*x2 <= 95, "umami_max")
m.addConstr(13*x1 + 6*x2 <= 130, "tastiness_max")

# Optimize the model
m.optimize()

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