To solve the given optimization problem using Gurobi, we first need to define the decision variables and the objective function. The decision variables in this case are the quantities of 'chicken thighs', 'apple pies', and 'knishes' that we want to determine.

The objective function is given as: minimize $3 \times \text{chicken thighs} + 2 \times \text{apple pies} + 6 \times \text{knishes}$.

We have several constraints based on the calcium content of each item and some additional constraints. Let's denote:
- $x_0$ as the quantity of 'chicken thighs',
- $x_1$ as the quantity of 'apple pies',
- $x_2$ as the quantity of 'knishes'.

Given information:
- Each chicken thigh contains 6 milligrams of calcium.
- Each apple pie contains 19 milligrams of calcium.
- Each knish contains 17 milligrams of calcium.

Constraints:
1. $6x_0 + 19x_1 \geq 57$ (at least 57 milligrams of calcium from chicken thighs and apple pies),
2. $6x_0 + 17x_2 \geq 59$ (at least 59 milligrams of calcium from chicken thighs and knishes),
3. $6x_0 + 19x_1 + 17x_2 \geq 59$ (at least 59 milligrams of calcium from all three items combined),
4. $-10x_0 + x_1 \geq 0$ (minus ten times the number of chicken thighs, plus the number of apple pies must be greater than or equal to zero).

All variables are continuous, meaning they can take any real value.

Here is how we can model this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define the decision variables as continuous
x0 = m.addVar(vtype=GRB.CONTINUOUS, name='chicken_thighs')
x1 = m.addVar(vtype=GRB.CONTINUOUS, name='apple_pies')
x2 = m.addVar(vtype=GRB.CONTINUOUS, name='knishes')

# Define the objective function
m.setObjective(3*x0 + 2*x1 + 6*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(6*x0 + 19*x1 >= 57, 'calcium_from_chicken_and_pies')
m.addConstr(6*x0 + 17*x2 >= 59, 'calcium_from_chicken_and_knishes')
m.addConstr(6*x0 + 19*x1 + 17*x2 >= 59, 'calcium_from_all_items')
m.addConstr(-10*x0 + x1 >= 0, 'chicken_thighs_vs_apple_pies')

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Chicken thighs: {x0.x}")
    print(f"Apple pies: {x1.x}")
    print(f"Knishes: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```