To solve this problem using Gurobi in Python, we'll define the variables and constraints as per the given conditions. The goal here is not to optimize a specific objective function but rather to express all given constraints within the model.

```python
from gurobi import *

# Define the model
m = Model("Nutrition_Problem")

# Variables definition
knishes, peanutbutter_sandwiches, apples, strawberries, fruit_salads, hamburgers = \
    m.addVars(6, lb=0, name=["knishes", "peanutbutter_sandwiches", "apples", "strawberries", "fruit_salads", "hamburgers"])

# Constraints
m.addConstr(knishes + peanutbutter_sandwiches >= 21) # Minimum total of knishes and peanutbutter sandwiches
m.addConstr(peanutbutter_sandwiches <= 11) # Maximum number of peanutbutter sandwiches

# Assuming we need to define the cost coefficients for each item
cost_knishes, cost_peanutbutter_sandwiches, cost_apples, cost_strawberries, cost_fruit_salads, cost_hamburgers = \
    5, 3, 2, 1.5, 4, 6 # Example costs per unit

# Cost constraints
m.addConstr(cost_knishes * knishes + cost_peanutbutter_sandwiches * peanutbutter_sandwiches <= 157) # Knishes and peanutbutter sandwiches cost constraint
m.addConstr(cost_knishes * knishes + cost_strawberries * strawberries <= 228) # Knishes and strawberries cost constraint
m.addConstr(cost_knishes * knishes + cost_hamburgers * hamburgers <= 191) # Knishes and hamburgers cost constraint
m.addConstr(cost_strawberries * strawberries + cost_fruit_salads * fruit_salads <= 65) # Strawberries and fruit salads cost constraint
m.addConstr(cost_peanutbutter_sandwiches * peanutbutter_sandwiches + cost_apples * apples <= 135) # Peanutbutter sandwiches and apples cost constraint

# Additional constraints as per problem statement (for illustration; actual values might vary)
protein_knishes, protein_peanutbutter_sandwiches, protein_apples, protein_strawberries, protein_fruit_salads, protein_hamburgers = \
    10, 8, 0.5, 1, 2, 20 # Example protein content per unit

# Protein constraints
m.addConstr(protein_knishes * knishes + protein_peanutbutter_sandwiches * peanutbutter_sandwiches + 
            protein_apples * apples + protein_strawberries * strawberries + protein_fruit_salads * fruit_salads + 
            protein_hamburgers * hamburgers >= 1000) # Minimum total protein

m.addConstr(-4*peanutbutter_sandwiches + 2*fruit_salads + 5*hamburgers >= 0)

# Solve the model
m.optimize()
```

```python
# To print solution values (assuming a feasible solution exists)
if m.status == GRB.OPTIMAL:
    for v in m.getVars():
        print('%s %f' % (v.varName, v.x))
else:
    print('No optimal solution found')
```